List of datasets for machine learning research

From HandWiki

These datasets are used for machine-learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5]

Image data

Datasets consisting primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification

Facial recognition

In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces.

Dataset name Brief description Preprocessing Instances Format Default task Created (updated) Reference Creator
FERET (facial recognition technology) 11338 images of 1199 individuals in different positions and at different times. None. 11,338 Images Classification, face recognition 2003 [6][7] United States Department of Defense
CMU Pose, Illumination, and Expression (PIE) 41,368 color images of 68 people in 13 different poses. Images labeled with expressions. 41,368 Images, text Classification, face recognition 2000 [8][9] R. Gross et al.
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) 7,356 video and audio recordings of 24 professional actors. 8 emotions each at two intensities. Files labelled with expression. Perceptual validation ratings provided by 319 raters. 7,356 Video, sound files Classification, face recognition, voice recognition 2018 [10][11] S.R. Livingstone and F.A. Russo
SCFace Color images of faces at various angles. Location of facial features extracted. Coordinates of features given. 4,160 Images, text Classification, face recognition 2011 [12][13] M. Grgic et al.
YouTube Faces DB Videos of 1,595 different people gathered from YouTube. Each clip is between 48 and 6,070 frames. Identity of those appearing in videos and descriptors. 3,425 videos Video, text Video classification, face recognition 2011 [14][15] L. Wolf et al.
300 videos in-the-Wild 114 videos annotated for facial landmark tracking. The 68 landmark mark-up is applied to every frame. None 114 videos, 218,000 frames. Video, annotation file. Facial landmark tracking. 2015 [16] Shen, Jie et al.
Grammatical Facial Expressions Dataset Grammatical Facial Expressions from Brazilian Sign Language. Microsoft Kinect features extracted. 27,965 Text Facial gesture recognition 2014 [17] F. Freitas et al.
CMU Face Images Dataset Images of faces. Each person is photographed multiple times to capture different expressions. Labels and features. 640 Images, Text Face recognition 1999 [18][19] T. Mitchell
Yale Face Database Faces of 15 individuals in 11 different expressions. Labels of expressions. 165 Images Face recognition 1997 [20][21] J. Yang et al.
Cohn-Kanade AU-Coded Expression Database Large database of images with labels for expressions. Tracking of certain facial features. 500+ sequences Images, text Facial expression analysis 2000 [22][23] T. Kanade et al.
FaceScrub Images of public figures scrubbed from image searching. Name and m/f annotation. 107,818 Images, text Face recognition 2014 [24][25] H. Ng et al.
BioID Face Database Images of faces with eye positions marked. Manually set eye positions. 1521 Images, text Face recognition 2001 [26][27] BioID
Skin Segmentation Dataset Randomly sampled color values from face images. B, G, R, values extracted. 245,057 Text Segmentation, classification 2012 [28][29] R. Bhatt.
Bosphorus 3D Face image database. 34 action units and 6 expressions labeled; 24 facial landmarks labeled. 4652

Images, text

Face recognition, classification 2008 [30][31] A Savran et al.
UOY 3D-Face neutral face, 5 expressions: anger, happiness, sadness, eyes closed, eyebrows raised. labeling. 5250

Images, text

Face recognition, classification 2004 [32][33] University of York
CASIA Expressions: Anger, smile, laugh, surprise, closed eyes. None. 4624

Images, text

Face recognition, classification 2007 [34][35] Institute of Automation, Chinese Academy of Sciences
CASIA Expressions: Anger Disgust Fear Happiness Sadness Surprise None. 480 Annotated Visible Spectrum and Near Infrared Video captures at 25 frames per second Face recognition, classification 2011 [36] Zhao, G. et al.
BU-3DFE neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted. None. 2500 Images, text Facial expression recognition, classification 2006 [37] Binghamton University
Face Recognition Grand Challenge Dataset Up to 22 samples for each subject. Expressions: anger, happiness, sadness, surprise, disgust, puffy. 3D Data. None. 4007 Images, text Face recognition, classification 2004 [38][39] National Institute of Standards and Technology
Gavabdb Up to 61 samples for each subject. Expressions neutral face, smile, frontal accentuated laugh, frontal random gesture. 3D images. None. 549 Images, text Face recognition, classification 2008 [40][41] King Juan Carlos University
3D-RMA Up to 100 subjects, expressions mostly neutral. Several poses as well. None. 9971 Images, text Face recognition, classification 2004 [42][43] Royal Military Academy (Belgium)
SoF 112 persons (66 males and 46 females) wear glasses under different illumination conditions. A set of synthetic filters (blur, occlusions, noise, and posterization ) with different level of difficulty. 42,592 (2,662 original image × 16 synthetic image) Images, Mat file Gender classification, face detection, face recognition, age estimation, and glasses detection 2017 [44][45] Afifi, M. et al.
IMDB-WIKI IMDB and Wikipedia face images with gender and age labels. None 523,051 Images Gender classification, face detection, face recognition, age estimation 2015 [46] R. Rothe, R. Timofte, L. V. Gool

Action recognition

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Human Motion DataBase (HMDB51) 51 action categories, each containing at least 101 clips, extracted from a range of sources. None. 6,766 video clips video clips Action classification 2011 [47] H. Kuehne et al.
TV Human Interaction Dataset Videos from 20 different TV shows for prediction social actions: handshake, high five, hug, kiss and none. None. 6,766 video clips video clips Action prediction 2013 [48] Patron-Perez, A. et al.
UT Interaction People acting out one of 6 actions (shake-hands, point, hug, push, kick, and punch) sometimes with multiple groups in the same video clip. None. 120 video clips video clips Action prediction 2009 [49] Ryoo, M. S. et al.
UT Kinect 10 different people performing one of 6 actions (walk, sit down, stand up, pick up, carry, throw, push, pull, wave hands and clap hands) in an office setting. None. 200 video clips with depth information at 15 frames per second video clips with depth information Action classification 2012 [50] Xia, L. et al.
SBU Interact Seven participants performing one of 8 actions together (approaching, departing, pushing, kicking, punching, exchanging objects, hugging, and shaking hands) in an office setting. None. Around 300 interactions video clips with depth information Action classification 2012 [51] Yun, K. et al.
Berkeley Multimodal Human Action Database (MHAD) Recordings of a single person performing 12 actions MoCap pre-processing 660 action samples 8 PhaseSpace Motion Capture, 2 Stereo Cameras, 4 Quad Cameras, 6 accelerometers, 4 microphones Action classification 2013 [52] Ofli, F. et al.
UCF 101 Dataset Self described as "a dataset of 101 human actions classes from videos in the wild." Dataset is large with over 27 hours of video. Actions classified and labeled. 13,000 Video, images, text Classification, action detection 2012 [53][54] K. Soomro et al.
THUMOS Dataset Large video dataset for action classification. Actions classified and labeled. 45M frames of video Video, images, text Classification, action detection 2013 [55][56] Y. Jiang et al.
Activitynet Large video dataset for activity recognition and detection. Actions classified and labeled. 10,024 Video, images, text Classification, action detection 2015 [57] Heilbron et al.
MSP-AVATAR Improvised scenarios annotated for discourse functions: contrast, confirmation/negation, question, uncertainty, suggest, giving orders, warn, inform, size description, using pronouns. Actions classified and labeled. 74 sessions Motion-captured video, audio Classification, action detection 2015 [58] Sadoughi, N. et al.
LILiR Twotalk Corpus Video datasets for non-verbal communication activity recognition: agreement, thinking, asking and understanding. Actions classified and labeled. 527 Video Action detection 2011 [59] Sheerman-Chase et al.
MEXAction2 Video dataset for action localization and spotting Actions classified and labeled. 1000 Video Action detection 2014 [60] Stoian et al.

Object detection and recognition

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Visual Genome Images and their description 108,000 images, text Image captioning 2016 [61] R. Krishna et al.
DAVIS: Densely Annotated VIdeo Segmentation 2017 150 video sequences containing 10459 frames with a total of 376 objects annotated. Dataset released for the 2017 DAVIS Challenge with a dedicated workshop co-located with CVPR 2017. The videos contain several types of objects and humans with a high quality segmentation annotation.In each video sequence multiple instances are annotated. 10,459 Frames annotated Video object segmentation 2017 [62] Pont-Tuset, J. et al.
DAVIS: Densely Annotated VIdeo Segmentation 2016 50 video sequences containing 3455 frames with a total of 50 objects annotated. Dataset released with the CVPR 2016 paper. The videos contain several types of objects and humans with a high quality segmentation annotation. In each video sequence a single instance is annotated. 3,455 Frames annotated Video object segmentation 2016 [63] Perazzi, F. et al.
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects 30 industry-relevant objects. 39K training and 10K test images from each of three sensors. Two types of 3D models for each object. 6D poses for all modeled objects in all images. Per-pixel labelling can be obtained by rendering of the object models at the ground truth poses. 49,000 RGB-D images, 3D object models 6D object pose estimation, object detection 2017 [64] T. Hodan et al.
Berkeley 3-D Object Dataset 849 images taken in 75 different scenes. About 50 different object classes are labeled. Object bounding boxes and labeling. 849 labeled images, text Object recognition 2014 [65][66] A. Janoch et al.
Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500) 500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300. Each image segmented by five different subjects on average. 500 Segmented images Contour detection and hierarchical image segmentation 2011 [67] University of California, Berkeley
Microsoft Common Objects in Context (COCO) complex everyday scenes of common objects in their natural context. Object highlighting, labeling, and classification into 91 object types. 2,500,000 Labeled images, text Object recognition 2015 [68][69] T. Lin et al.
SUN Database Very large scene and object recognition database. Places and objects are labeled. Objects are segmented. 131,067 Images, text Object recognition, scene recognition 2014 [70][71] J. Xiao et al.
ImageNet Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge Labeled objects, bounding boxes, descriptive words, SIFT features 14,197,122 Images, text Object recognition, scene recognition 2009 (2014) [72][73][74] J. Deng et al.
Open Images A Large set of images listed as having CC BY 2.0 license with image-level labels and bounding boxes spanning thousands of classes. Image-level labels, Bounding boxes 9,178,275 Images, text Classification, Object recognition 2017 [75]
TV News Channel Commercial Detection Dataset TV commercials and news broadcasts. Audio and video features extracted from still images. 129,685 Text Clustering, classification 2015 [76][77] P. Guha et al.
Statlog (Image Segmentation) Dataset The instances were drawn randomly from a database of 7 outdoor images and hand-segmented to create a classification for every pixel. Many features calculated. 2310 Text Classification 1990 [78] University of Massachusetts
Caltech 101 Pictures of objects. Detailed object outlines marked. 9146 Images Classification, object recognition. 2003 [79][80] F. Li et al.
Caltech-256 Large dataset of images for object classification. Images categorized and hand-sorted. 30,607 Images, Text Classification, object detection 2007 [81][82] G. Griffin et al.
SIFT10M Dataset SIFT features of Caltech-256 dataset. Extensive SIFT feature extraction. 11,164,866 Text Classification, object detection 2016 [83] X. Fu et al.
LabelMe Annotated pictures of scenes. Objects outlined. 187,240 Images, text Classification, object detection 2005 [84] MIT Computer Science and Artificial Intelligence Laboratory
Cityscapes Dataset Stereo video sequences recorded in street scenes, with pixel-level annotations. Metadata also included. Pixel-level segmentation and labeling 25,000 Images, text Classification, object detection 2016 [85] Daimler AG et al.
PASCAL VOC Dataset Large number of images for classification tasks. Labeling, bounding box included 500,000 Images, text Classification, object detection 2010 [86][87] M. Everingham et al.
CIFAR-10 Dataset Many small, low-resolution, images of 10 classes of objects. Classes labelled, training set splits created. 60,000 Images Classification 2009 [73][88] A. Krizhevsky et al.
CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [73][88] A. Krizhevsky et al.
Fashion-MNIST A MNIST-like fashion product database Classes labelled, training set splits created. 60,000 Images Classification 2017 [89] Zalando SE
notMNIST Some publicly available fonts and extracted glyphs from them to make a dataset similar to MNIST. There are 10 classes, with letters A-J taken from different fonts. Classes labelled, training set splits created. 500,000 Images Classification 2011 [90] Yaroslav Bulatov
German Traffic Sign Detection Benchmark Dataset Images from vehicles of traffic signs on German roads. These signs comply with UN standards and therefore are the same as in other countries. Signs manually labeled 900 Images Classification 2013 [91][92] S Houben et al.
KITTI Vision Benchmark Dataset Autonomous vehicles driving through a mid-size city captured images of various areas using cameras and laser scanners. Many benchmarks extracted from data. >100 GB of data Images, text Classification, object detection 2012 [93][94] A Geiger et al.
Linnaeus 5 dataset Images of 5 classes of objects. Classes labelled, training set splits created. 8000 Images Classification 2017 [95] Chaladze & Kalatozishvili
FieldSAFE Multi-modal dataset for obstacle detection in agriculture including stereo camera, thermal camera, web camera, 360-degree camera, lidar, radar, and precise localization. Classes labelled geographically. >400 GB of data Images and 3D point clouds Classification, object detection, object localization 2017 [96] M. Kragh et al.
11K Hands 11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification. None 11,076 hand images Images and (.mat, .txt, and .csv) label files Gender recognition and biometric identification 2017 [97] M Afifi
CORe50 Specifically designed for Continuous/Lifelong Learning and Object Recognition, is a collection of more than 500 videos (30fps) of 50 domestic objects belonging to 10 different categories. Classes labelled, training set splits created based on a 3-way, multi-runs benchmark. 164,866 RBG-D images images (.png or .pkl)

and (.pkl, .txt, .tsv) label files

Classification, Object recognition 2017 [98] V. Lomonaco and D. Maltoni

Handwriting and character recognition

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Artificial Characters Dataset Artificially generated data describing the structure of 10 capital English letters. Coordinates of lines drawn given as integers. Various other features. 6000 Text Handwriting recognition, classification 1992 [99] H. Guvenir et al.
Letter Dataset Upper case printed letters. 17 features are extracted from all images. 20,000 Text OCR, classification 1991 [100][101] D. Slate et al.
Character Trajectories Dataset Labeled samples of pen tip trajectories for people writing simple characters. 3-dimensional pen tip velocity trajectory matrix for each sample 2858 Text Handwriting recognition, classification 2008 [102][103] B. Williams
Chars74K Dataset Character recognition in natural images of symbols used in both English and Kannada 74,107 Character recognition, handwriting recognition, OCR, classification 2009 [104] T. de Campos
UJI Pen Characters Dataset Isolated handwritten characters Coordinates of pen position as characters were written given. 11,640 Text Handwriting recognition, classification 2009 [105][106] F. Prat et al.
Gisette Dataset Handwriting samples from the often-confused 4 and 9 characters. Features extracted from images, split into train/test, handwriting images size-normalized. 13,500 Images, text Handwriting recognition, classification 2003 [107] Yann LeCun et al.
MNIST database Database of handwritten digits. Hand-labeled. 60,000 Images, text Classification 1998 [108][109] National Institute of Standards and Technology
Optical Recognition of Handwritten Digits Dataset Normalized bitmaps of handwritten data. Size normalized and mapped to bitmaps. 5620 Images, text Handwriting recognition, classification 1998 [110] E. Alpaydin et al.
Pen-Based Recognition of Handwritten Digits Dataset Handwritten digits on electronic pen-tablet. Feature vectors extracted to be uniformly spaced. 10,992 Images, text Handwriting recognition, classification 1998 [111][112] E. Alpaydin et al.
Semeion Handwritten Digit Dataset Handwritten digits from 80 people. All handwritten digits have been normalized for size and mapped to the same grid. 1593 Images, text Handwriting recognition, classification 2008 [113] T. Srl
HASYv2 Handwritten mathematical symbols All symbols are centered and of size 32px x 32px. 168233 Images, text Classification 2017 [114] Martin Thoma

Aerial images

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Aerial Image Segmentation Dataset 80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0. Images manually segmented. 80 Images Aerial Classification, object detection 2013 [115][116] J. Yuan et al.
KIT AIS Data Set Multiple labeled training and evaluation datasets of aerial images of crowds. Images manually labeled to show paths of individuals through crowds. ~ 150 Images with paths People tracking, aerial tracking 2012 [117][118] M. Butenuth et al.
Wilt Dataset Remote sensing data of diseased trees and other land cover. Various features extracted. 4899 Images Classification, aerial object detection 2014 [119][120] B. Johnson
Forest Type Mapping Dataset Satellite imagery of forests in Japan. Image wavelength bands extracted. 326 Text Classification 2015 [121][122] B. Johnson
Overhead Imagery Research Data Set Annotated overhead imagery. Images with multiple objects. Over 30 annotations and over 60 statistics that describe the target within the context of the image. 1000 Images, text Classification 2009 [123][124] F. Tanner et al.
SpaceNet SpaceNet is a corpus of commercial satellite imagery and labeled training data. GeoTiff and GeoJSON files containing building footprints. >17533 Images Classification, Object Identification 2017 [125][126][127] DigitalGlobe, Inc.
UC Merced Land Use Dataset These images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the US. This is a 21 class land use image dataset meant for research purposes. There are 100 images for each class. 2,100 Image chips of 256x256, 30 cm (1 foot) GSD Land cover classification 2010 [128] Yi Yang and Shawn Newsam
SAT-4 Airborne Dataset Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. SAT-4 has four broad land cover classes, includes barren land, trees, grassland and a class that consists of all land cover classes other than the above three. 500,000 Images Classification 2015 [129] S. Basu et al.
SAT-6 Airborne Dataset Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. SAT-6 has six broad land cover classes, includes barren land, trees, grassland, roads, buildings and water bodies. 405,000 Images Classification 2015 [129] S. Basu et al.

Other images

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Quantum simulations of an electron in a two dimensional potential well Labelled images of raw input to a simulation of 2d Quantum mechanics Raw data (in HDF5 format) and output labels from quantum simulation 1.3 million images Labeled images Regression 2017 [130] K. Mills et al.
MPII Cooking Activities Dataset Videos and images of various cooking activities. Activity paths and directions, labels, fine-grained motion labeling, activity class, still image extraction and labeling. 881,755 frames Labeled video, images, text Classification 2012 [131][132] M. Rohrbach et al.
FAMOS Dataset 5,000 unique microstructures, all samples have been acquired 3 times with two different cameras. Original PNG files, sorted per camera and then per acquisition. MATLAB datafiles with one 16384 times 5000 matrix per camera per acquisition. 30,000 Images and .mat files Authentication 2012 [133] S. Voloshynovskiy, et al.
PharmaPack Dataset 1,000 unique classes with 54 images per class. Class labeling, many local descriptors, like SIFT and aKaZE, and local feature agreators, like Fisher Vector (FV). 54,000 Images and .mat files Fine-grain classification 2017 [134] O. Taran and S. Rezaeifar, et al.
Stanford Dogs Dataset Images of 120 breeds of dogs from around the world. Train/test splits and ImageNet annotations provided. 20,580 Images, text Fine-grain classification 2011 [135][136] A. Khosla et al.
The Oxford-IIIT Pet Dataset 37 categories of pets with roughly 200 images of each. Breed labeled, tight bounding box, foreground-background segmentation. ~ 7,400 Images, text Classification, object detection 2012 [136][137] O. Parkhi et al.
Corel Image Features Data Set Database of images with features extracted. Many features including color histogram, co-occurrence texture, and colormoments, 68,040 Text Classification, object detection 1999 [138][139] M. Ortega-Bindenberger et al.
Online Video Characteristics and Transcoding Time Dataset. Transcoding times for various different videos and video properties. Video features given. 168,286 Text Regression 2015 [140] T. Deneke et al.
Microsoft Sequential Image Narrative Dataset (SIND) Dataset for sequential vision-to-language Descriptive caption and storytelling given for each photo, and photos are arranged in sequences 81,743 Images, text Visual storytelling 2016 [141] Microsoft Research
Caltech-UCSD Birds-200-2011 Dataset Large dataset of images of birds. Part locations for birds, bounding boxes, 312 binary attributes given 11,788 Images, text Classification 2011 [142][143] C. Wah et al.
YouTube-8M Large and diverse labeled video dataset YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities 8 million Video, text Video classification 2016 [144][145] S. Abu-El-Haija et al.
YFCC100M Large and diverse labeled image and video dataset Flickr Videos and Images and associated description, titles, tags, and other metadata (such as EXIF and geotags) 100 million Video, Image, Text Video and Image classification 2016 [146][147] B. Thomee et al.
Discrete LIRIS-ACCEDE Short videos annotated for valence and arousal. Valence and arousal labels. 9800 Video Video emotion elicitation detection 2015 [148] Y. Baveye et al.
Continuous LIRIS-ACCEDE Long videos annotated for valence and arousal while also collecting Galvanic Skin Response. Valence and arousal labels. 30 Video Video emotion elicitation detection 2015 [149] Y. Baveye et al.
MediaEval LIRIS-ACCEDE Extension of Discrete LIRIS-ACCEDE including annotations for violence levels of the films. Violence, valence and arousal labels. 10900 Video Video emotion elicitation detection 2015 [150] Y. Baveye et al.
Leeds Sports Pose Articulated human pose annotations in 2000 natural sports images from Flickr. Rough crop around single person of interest with 14 joint labels 2000 Images plus .mat file labels Human pose estimation 2010 [151] S. Johnson and M. Everingham
Leeds Sports Pose Extended Training Articulated human pose annotations in 10,000 natural sports images from Flickr. 14 joint labels via crowdsourcing 10000 Images plus .mat file labels Human pose estimation 2011 [152] S. Johnson and M. Everingham
MCQ Dataset 6 different real multiple choice-based exams (735 answer sheets and 33,540 answer boxes) to evaluate computer vision techniques and systems developed for multiple choice test assessment systems. None 735 answer sheets and 33,540 answer boxes Images and .mat file labels Development of multiple choice test assessment systems 2017 [153][154] Afifi, M. et al.
Surveillance Videos Real surveillance videos cover a large surveillance time (7 days with 24 hours each). None 19 surveillance videos (7 days with 24 hours each). Videos Data compression 2016 [155] Taj-Eddin, I. A. T. F. et al.
Can We See Photosynthesis? 32 videos for eight live and eight dead leaves recorded under both DC and AC lighting conditions. None 32 videos Videos Liveness detection of plants 2017 [156] Taj-Eddin, I. A. T. F. et al.

Text data

Datasets consisting primarily of text for tasks such as natural language processing, sentiment analysis, translation, and cluster analysis.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Stanford Sentiment Treebank Positive and negative movie reviews 11k Text Sentiment analysis 2005 [1] Bo Pang & Lillian Lee
IMDB Reviews Positive and negative movie reviews 50k Text Sentiment analysis 2011 [2] Maas et al.
Movie Review Data Positive and negative movie reviews 2k Text Sentiment analysis 2005 [3] Bo Pang & Lillian Lee
Amazon reviews US product reviews from None. ~ 82M Text Classification, sentiment analysis 2015 [157] McAuley et al.
OpinRank Review Dataset Reviews of cars and hotels from and TripAdvisor respectively. None. 42,230 / ~259,000 respectively Text Sentiment analysis, clustering 2011 [158][159] K. Ganesan et al.
MovieLens 22,000,000 ratings and 580,000 tags applied to 33,000 movies by 240,000 users. None. ~ 22M Text Regression, clustering, classification 2016 [160] GroupLens Research
Yahoo! Music User Ratings of Musical Artists Over 10M ratings of artists by Yahoo users. None described. ~ 10M Text Clustering, regression 2004 [161][162] Yahoo!
Car Evaluation Data Set Car properties and their overall acceptability. Six categorical features given. 1728 Text Classification 1997 [163][164] M. Bohanec
YouTube Comedy Slam Preference Dataset User vote data for pairs of videos shown on YouTube. Users voted on funnier videos. Video metadata given. 1,138,562 Text Classification 2012 [165][166] Google
Skytrax User Reviews Dataset User reviews of airlines, airports, seats, and lounges from Skytrax. Ratings are fine-grain and include many aspects of airport experience. 41396 Text Classification, regression 2015 [167] Q. Nguyen
Teaching Assistant Evaluation Dataset Teaching assistant reviews. Features of each instance such as class, class size, and instructor are given. 151 Text Classification 1997 [168][169] W. Loh et al.

News articles

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
NYSK Dataset English news articles about the case relating to allegations of sexual assault against the former IMF director Dominique Strauss-Kahn. Filtered and presented in XML format. 10,421 XML, text Sentiment analysis, topic extraction 2013 [170] Dermouche, M. et al.
The Reuters Corpus Volume 1 Large corpus of Reuters news stories in English. Fine-grain categorization and topic codes. 810,000 Text Classification, clustering, summarization 2002 [171] Reuters
The Reuters Corpus Volume 2 Large corpus of Reuters news stories in multiple languages. Fine-grain categorization and topic codes. 487,000 Text Classification, clustering, summarization 2005 [172] Reuters
Thomson Reuters Text Research Collection Large corpus of news stories. Details not described. 1,800,370 Text Classification, clustering, summarization 2009 [173] T. Rose et al.
Saudi Newspapers Corpus 31,030 Arabic newspaper articles. Metadata extracted. 31,030 JSON Summarization, clustering 2015 [174] M. Alhagri
RE3D (Relationship and Entity Extraction Evaluation Dataset) Entity and Relation marked data from various news and government sources. Sponsored by Dstl Filtered, categorisation using Baleen types not known JSON Classification, Entity and Relation recognition 2017 [175] Dstl
ABC Australia News Corpus Entire news corpus of ABC Australia from 2003 to 2017 Publish date and headlines 1,082,477 CSV Clustering, Events, Sentiment 2017 [176] R. Kulkarni
Examiner Pseudo-News Corpus Clickbait, spam, crowd-sourced headlines from 2010 to 2015 Publish date and headlines 3,089,781 CSV Clustering, Events, Sentiment 2017 [177] R. Kulkarni
Worldwide News - Aggregate of 20K Feeds One week snapshot of all online headlines in 20+ languages Publish time, URL and headlines 1,398,431 CSV Clustering, Events, Language Detection 2017 [178] R. Kulkarni
Reuters News Wire Headline 11+ Years of timestamped events published on the news-wire Publish time, Headline Text 16,121,000 CSV NLP, Computational Linguistics, Events 2018 [179] R. Kulkarni
The Irish Times The Irish Times IRS 12 Years of Events From Ireland Publish time, Headline Text 1,422,000 CSV NLP, Computational Linguistics, Events 2018 [180] R. Kulkarni


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Enron Email Dataset Emails from employees at Enron organized into folders. Attachments removed, invalid email addresses converted to or ~ 500,000 Text Network analysis, sentiment analysis 2004 (2015) [181][182] Klimt, B. and Y. Yang
Ling-Spam Dataset Corpus containing both legitimate and spam emails. Four version of the corpus involving whether or not a lemmatiser or stop-list was enabled. Text Classification 2000 [183][184] Androutsopoulos, J. et al.
SMS Spam Collection Dataset Collected SMS spam messages. None. 5574 Text Classification 2011 [185][186] T. Almeida et al.
Twenty Newsgroups Dataset Messages from 20 different newsgroups. None. 20,000 Text Natural language processing 1999 [187] T. Mitchell et al.
Spambase Dataset Spam emails. Many text features extracted. 4601 Text Spam detection, classification 1999 [188] M. Hopkins et al.

Twitter and tweets

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
MovieTweetings Movie rating dataset based on public and well-structured tweets ~710,000 Text Classification, regression 2018 [189] S. Dooms
Twitter100k Pairs of images and tweets 100,000 Text and Images Cross-media retrieval 2017 [190][191] Y. Hu, et al.
Sentiment140 Tweet data from 2009 including original text, time stamp, user and sentiment. Classified using distant supervision from presence of emoticon in tweet. 1,578,627 Tweets, comma, separated values Sentiment analysis 2009 [192][193] A. Go et al.
ASU Twitter Dataset Twitter network data, not actual tweets. Shows connections between a large number of users. None. 11,316,811 users, 85,331,846 connections Text Clustering, graph analysis 2009 [194][195] R. Zafarani et al.
SNAP Social Circles: Twitter Database Large Twitter network data. Node features, circles, and ego networks. 1,768,149 Text Clustering, graph analysis 2012 [196][197] J. McAuley et al.
Twitter Dataset for Arabic Sentiment Analysis Arabic tweets. Samples hand-labeled as positive or negative. 2000 Text Classification 2014 [198][199] N. Abdulla
Buzz in Social Media Dataset Data from Twitter and Tom's Hardware. This dataset focuses on specific buzz topics being discussed on those sites. Data is windowed so that the user can attempt to predict the events leading up to social media buzz. 140,000 Text Regression, Classification 2013 [200][201] F. Kawala et al.
Paraphrase and Semantic Similarity in Twitter (PIT) This dataset focuses on whether tweets have (almost) same meaning/information or not. Manually labeled. tokenization, part-of-speech and named entity tagging 18,762 Text Regression, Classification 2015 [202][203] Xu et al.
Geoparse Twitter benchmark dataset This dataset contains tweets during different news events in different countries. Manually labeled location mentions. location annotations added to JSON metadata 6,386 Tweets, JSON Classification, Information Extraction 2014 [204][205] S.E. Middleton et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
NPS Chat Corpus Posts from age-specific online chat rooms. Hand privacy masked, tagged for part of speech and dialogue-act. ~ 500,000 XML NLP, programming, linguistics 2007 [206] Forsyth, E., Lin, J., & Martell, C.
Twitter Triple Corpus A-B-A triples extracted from Twitter. 4,232 Text NLP 2016 [207] Sordini, A. et al.
UseNet Corpus UseNet forum postings. Anonymized e-mails and URLs. Omitted documents with lengths <500 words or >500,000 words, or that were <90% English. 7 billion Text 2011 [208] Shaoul, C., & Westbury C.
NUS SMS Corpus SMS messages collected between two users, with timing analysis. ~ 10,000 XML NLP 2011 [209] KAN, M
Reddit All Comments Corpus All Reddit comments (as of 2015). ~ 1.7 billion JSON NLP, research 2015 [210] Stuck_In_the_Matrix
Ubuntu Dialogue Corpus Dialogues extracted from Ubuntu chat stream on IRC. CSV Dialogue Systems Research 2015 [211] Lowe, R. et al.

Other text

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Web of Science Dataset Hierarchical Datasets for Text Classification None. 46,985 Text Classification,


2017 [212][213] K. Kowsari et al.
Legal Case Reports Federal Court of Australia cases from 2006 to 2009. None. 4,000 Text Summarization,

citation analysis

2012 [214][215] F. Galgani et al.
Blogger Authorship Corpus Blog entries of 19,320 people from Blogger self-provided gender, age, industry, and astrological sign. 681,288 Text Sentiment analysis, summarization, classification 2006 [216][217] J. Schler et al.
Social Structure of Facebook Networks Large dataset of the social structure of Facebook. None. 100 colleges covered Text Network analysis, clustering 2012 [218][219] A. Traud et al.
Dataset for the Machine Comprehension of Text Stories and associated questions for testing comprehension of text. None. 660 Text Natural language processing, machine comprehension 2013 [220][221] M. Richardson et al.
The Penn Treebank Project Naturally occurring text annotated for linguistic structure. Text is parsed into semantic trees. ~ 1M words Text Natural language processing, summarization 1995 [222][223] M. Marcus et al.
DEXTER Dataset Task given is to determine, from features given, which articles are about corporate acquisitions. Features extracted include word stems. Distractor features included. 2600 Text Classification 2008 [224] Reuters
Google Books N-grams N-grams from a very large corpus of books None. 2.2 TB of text Text Classification, clustering, regression 2011 [225][226] Google
Personae Corpus Collected for experiments in Authorship Attribution and Personality Prediction. Consists of 145 Dutch-language essays. In addition to normal texts, syntactically annotated texts are given. 145 Text Classification, regression 2008 [227][228] K. Luyckx et al.
CNAE-9 Dataset Categorization task for free text descriptions of Brazilian companies. Word frequency has been extracted. 1080 Text Classification 2012 [229][230] P. Ciarelli et al.
Sentiment Labeled Sentences Dataset 3000 sentiment labeled sentences. Sentiment of each sentence has been hand labeled as positive or negative. 3000 Text Classification, sentiment analysis 2015 [231][232] D. Kotzias
BlogFeedback Dataset Dataset to predict the number of comments a post will receive based on features of that post. Many features of each post extracted. 60,021 Text Regression 2014 [233][234] K. Buza
Stanford Natural Language Inference (SNLI) Corpus Image captions matched with newly constructed sentences to form entailment, contradiction, or neutral pairs. Entailment class labels, syntactic parsing by the Stanford PCFG parser 570,000 Text Natural language inference/recognizing textual entailment 2015 [235] S. Bowman et al.
DSL Corpus Collection (DSLCC) A multilingual collection of short excerpts of journalistic texts in similar languages and dialects. None 294,000 phrases Text Discriminating between similar languages 2017 [236] Tan, Liling et al.
Urban Dictionary Dataset Corpus of words, votes and definitions User names anonymised 2,606,522 CSV NLP, Machine comprehension 2016-05 [237] Anonymous

Sound data

Datasets of sounds and sound features.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Zero Resource Speech Challenge 2015 Spontaneous speech (English), Read speech (Xitsonga). raw wav English: 5h, 12 speakers; Xitsonga: 2h30; 24 speakers sound Unsupervised discovery of speech features/subword units/word units 2015 [238][239] Versteegh et al.
Parkinson Speech Dataset Multiple recordings of people with and without Parkinson's Disease. Voice features extracted, disease scored by physician using unified Parkinson's disease rating scale 1,040 Text Classification, regression 2013 [240][241] B. E. Sakar et al.
Spoken Arabic Digits Spoken Arabic digits from 44 male and 44 female. Time-series of mel-frequency cepstrum coefficients. 8,800 Text Classification 2010 [242][243] M. Bedda et al.
ISOLET Dataset Spoken letter names. Features extracted from sounds. 7797 Text Classification 1994 [244][245] R. Cole et al.
Japanese Vowels Dataset Nine male speakers uttered two Japanese vowels successively. Applied 12-degree linear prediction analysis to it to obtain a discrete-time series with 12 cepstrum coefficients. 640 Text Classification 1999 [246][247] M. Kudo et al.
Parkinson's Telemonitoring Dataset Multiple recordings of people with and without Parkinson's Disease. Sound features extracted. 5875 Text Classification 2009 [248][249] A. Tsanas et al.
TIMIT Recordings of 630 speakers of eight major dialects of American English, each reading ten phonetically rich sentences. Speech is lexically and phonemically transcribed. 6300 Text Speech recognition, classification. 1986 [250][251] J. Garofolo et al.
Arabic Speech Corpus A single-speaker, Modern Standard Arabic (MSA) speech corpus with phonetic and orthographic transcripts aligned to phoneme level Speech is orthographically and phonetically transcribed with stress marks. ~1900 Text, WAV Speech Synthesis, Speech Recognition, Corpus Alignment, Speech Therapy, Education. 2016 [252] N. Halabi
Persian Consonant Vowel Combination (PCVC) Speech Dataset Persian phonemes Speech Dataset. raw mat: Speech samples with phoneme level exact label Persian: 1794 (To be continued...) Speech sound samples Speech phoneme and speaker classification 2018 [253] PCVC on GitHub S.Malekzadeh et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Geographical Original of Music Data Set Audio features of music samples from different locations. Audio features extracted using MARSYAS software. 1,059 Text Geographical classification, clustering 2014 [254][255] F. Zhou et al.
Million Song Dataset Audio features from one million different songs. Audio features extracted. 1M Text Classification, clustering 2011 [256][257] T. Bertin-Mahieux et al.
Free Music Archive Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [258] M. Defferrard et al.
Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text Classification 2014 [259][260] D. Radicioni et al.

Other sounds

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
UrbanSound Labeled sound recordings of sounds like air conditioners, car horns and children playing. Sorted into folders by class of events as well as metadata in a JSON file and annotations in a CSV file. 1,059 Sound


Classification 2014 [261][262] J. Salamon et al.
AudioSet 10-second sound snippets from YouTube videos, and an ontology of over 500 labels. 128-d PCA'd VGG-ish features every 1 second. 2,084,320 Text (CSV) and TensorFlow Record files Classification 2017 [263] J. Gemmeke et al., Google
Bird Audio Detection challenge Audio from environmental monitoring stations, plus crowdsourced recordings 17,000+ Classification 2016 (2018) [264][265] Queen Mary University and IEEE Signal Processing Society

Signal data

Datasets containing electric signal information requiring some sort of Signal processing for further analysis.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Witty Worm Dataset Dataset detailing the spread of the Witty worm and the infected computers. Split into a publicly available set and a restricted set containing more sensitive information like IP and UDP headers. 55,909 IP addresses Text Classification 2004 [266][267] Center for Applied Internet Data Analysis
Cuff-Less Blood Pressure Estimation Dataset Cleaned vital signals from human patients which can be used to estimate blood pressure. 125 Hz vital signs have been cleaned. 12,000 Text Classification, regression 2015 [268][269] M. Kachuee et al.
Gas Sensor Array Drift Dataset Measurements from 16 chemical sensors utilized in simulations for drift compensation. Extensive number of features given. 13,910 Text Classification 2012 [270][271] A. Vergara
Servo Dataset Data covering the nonlinear relationships observed in a servo-amplifier circuit. Levels of various components as a function of other components are given. 167 Text Regression 1993 [272][273] K. Ullrich
UJIIndoorLoc-Mag Dataset Indoor localization database to test indoor positioning systems. Data is magnetic field based. Train and test splits given. 40,000 Text Classification, regression, clustering 2015 [274][275] D. Rambla et al.
Sensorless Drive Diagnosis Dataset Electrical signals from motors with defective components. Statistical features extracted. 58,508 Text Classification 2015 [276][277] M. Bator


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Wearable Computing: Classification of Body Postures and Movements (PUC-Rio) People performing five standard actions while wearing motion tackers. None. 165,632 Text Classification 2013 [278][279] Pontifical Catholic University of Rio de Janeiro
Gesture Phase Segmentation Dataset Features extracted from video of people doing various gestures. Features extracted aim at studying gesture phase segmentation. 9900 Text Classification, clustering 2014 [280][281] R. Madeo et a
Vicon Physical Action Data Set Dataset 10 normal and 10 aggressive physical actions that measure the human activity tracked by a 3D tracker. Many parameters recorded by 3D tracker. 3000 Text Classification 2011 [282][283] T. Theodoridis
Daily and Sports Activities Dataset Motor sensor data for 19 daily and sports activities. Many sensors given, no preprocessing done on signals. 9120 Text Classification 2013 B. Barshan et al.
Human Activity Recognition Using Smartphones Dataset Gyroscope and accelerometer data from people wearing smartphones and performing normal actions. Actions performed are labeled, all signals preprocessed for noise. 10,299 Text Classification 2012 [284][285] J. Reyes-Ortiz et al.
Australian Sign Language Signs Australian sign language signs captured by motion-tracking gloves. None. 2565 Text Classification 2002 [286][287] M. Kadous
Weight Lifting Exercises monitored with Inertial Measurement Units Five variations of the biceps curl exercise monitored with IMUs. Some statistics calculated from raw data. 39,242 Text Classification 2013 [288][289] W. Ugulino et al.
sEMG for Basic Hand movements Dataset Two databases of surface electromyographic signals of 6 hand movements. None. 3000 Text Classification 2014 [290][291] C. Sapsanis et al.
REALDISP Activity Recognition Dataset Evaluate techniques dealing with the effects of sensor displacement in wearable activity recognition. None. 1419 Text Classification 2014 [291][292] O. Banos et al.
Heterogeneity Activity Recognition Dataset Data from multiple different smart devices for humans performing various activities. None. 43,930,257 Text Classification, clustering 2015 [293][294] A. Stisen et al.
Indoor User Movement Prediction from RSS Data Temporal wireless network data that can be used to track the movement of people in an office. None. 13,197 Text Classification 2016 [295][296] D. Bacciu
PAMAP2 Physical Activity Monitoring Dataset 18 different types of physical activities performed by 9 subjects wearing 3 IMUs. None. 3,850,505 Text Classification 2012 [297] A. Reiss
OPPORTUNITY Activity Recognition Dataset Human Activity Recognition from wearable, object, and ambient sensors is a dataset devised to benchmark human activity recognition algorithms. None. 2551 Text Classification 2012 [298][299] D. Roggen et al.
Real World Activity Recognition Dataset Human Activity Recognition from wearable devices. Distinguishes between seven on-body device positions and comprises six different kinds of sensors. None. 3,150,000 (per sensor) Text Classification 2016 [300] T. Sztyler et al.
Toronto Rehab Stroke Pose Dataset 3D human pose estimates (Kinect) of stroke patients and healthy participants performing a set of tasks using a stroke rehabilitation robot. None. 10 healthy person and 9 stroke survivors (3500-6000 frames per person) CSV Classification 2017 [301][302][303] E. Dolatabadi et al.

Other signals

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Wine Dataset Chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. 13 properties of each wine are given 178 Text Classification, regression 1991 [304][305] M. Forina et al.
Combined Cycle Power Plant Data Set Data from various sensors within a power plant running for 6 years. None 9568 Text Regression 2014 [306][307] P. Tufekci et al.

Physical data

Datasets from physical systems

High-energy physics

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
HIGGS Dataset Monte Carlo simulations of particle accelerator collisions. 28 features of each collision are given. 11M Text Classification 2014 [308][309][310] D. Whiteson
HEPMASS Dataset Monte Carlo simulations of particle accelerator collisions. Goal is to separate the signal from noise. 28 features of each collision are given. 10,500,000 Text Classification 2016 [309][310][311] D. Whiteson


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Yacht Hydrodynamics Dataset Yacht performance based on dimensions. Six features are given for each yacht. 308 Text Regression 2013 [312][313] R. Lopez
Robot Execution Failures Dataset 5 data sets that center around robotic failure to execute common tasks. Integer valued features such as torque and other sensor measurements. 463 Text Classification 1999 [314] L. Seabra et al.
Pittsburgh Bridges Dataset Design description is given in terms of several properties of various bridges. Various bridge features are given. 108 Text Classification 1990 [315][316] Y. Reich et al.
Automobile Dataset Data about automobiles, their insurance risk, and their normalized losses. Car features extracted. 205 Text Regression 1987 [317][318] J. Schimmer et al.
Auto MPG Dataset MPG data for cars. Eight features of each car given. 398 Text Regression 1993 [319] Carnegie Mellon University
Energy Efficiency Dataset Heating and cooling requirements given as a function of building parameters. Building parameters given. 768 Text Classification, regression 2012 [320][321] A. Xifara et al.
Airfoil Self-Noise Dataset A series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections. Data about frequency, angle of attack, etc., are given. 1503 Text Regression 2014 [322] R. Lopez
Challenger USA Space Shuttle O-Ring Dataset Attempt to predict O-ring problems given past Challenger data. Several features of each flight, such as launch temperature, are given. 23 Text Regression 1993 [323][324] D. Draper et al.
Statlog (Shuttle) Dataset NASA space shuttle datasets. Nine features given. 58,000 Text Classification 2002 [325] NASA


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Volcanoes on Venus – JARtool experiment Dataset Venus images returned by the Magellan spacecraft. Images are labeled by humans. not given Images Classification 1991 [326][327] M. Burl
MAGIC Gamma Telescope Dataset Monte Carlo generated high-energy gamma particle events. Numerous features extracted from the simulations. 19,020 Text Classification 2007 [327][328] R. Bock
Solar Flare Dataset Measurements of the number of certain types of solar flare events occurring in a 24-hour period. Many solar flare-specific features are given. 1389 Text Regression, classification 1989 [329] G. Bradshaw

Earth science

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Volcanoes of the World Volcanic eruption data for all known volcanic events on earth. Details such as region, subregion, tectonic setting, dominant rock type are given. 1535 Text Regression, classification 2013 [330] E. Venzke et al.
Seismic-bumps Dataset Seismic activities from a coal mine. Seismic activity was classified as hazardous or not. 2584 Text Classification 2013 [331][332] M. Sikora et al.

Other physical

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Concrete Compressive Strength Dataset Dataset of concrete properties and compressive strength. Nine features are given for each sample. 1030 Text Regression 2007 [333][334] I. Yeh
Concrete Slump Test Dataset Concrete slump flow given in terms of properties. Features of concrete given such as fly ash, water, etc. 103 Text Regression 2009 [335][336] I. Yeh
Musk Dataset Predict if a molecule, given the features, will be a musk or a non-musk. 168 features given for each molecule. 6598 Text Classification 1994 [337] Arris Pharmaceutical Corp.
Steel Plates Faults Dataset Steel plates of 7 different types. 27 features given for each sample. 1941 Text Classification 2010 [338] Semeion Research Center

Biological data

Datasets from biological systems.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
EEG Database Study to examine EEG correlates of genetic predisposition to alcoholism. Measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9 ms epoch) for 1 second. 122 Text Classification 1999 [339][340] H. Begleiter
P300 Interface Dataset Data from nine subjects collected using P300-based brain-computer interface for disabled subjects. Split into four sessions for each subject. MATLAB code given. 1,224 Text Classification 2008 [341][342] U. Hoffman et al.
Heart Disease Data Set Attributed of patients with and without heart disease. 75 attributes given for each patient with some missing values. 303 Text Classification 1988 [343][344] A. Janosi et al.
Breast Cancer Wisconsin (Diagnostic) Dataset Dataset of features of breast masses. Diagnoses by physician is given. 10 features for each sample are given. 569 Text Classification 1995 [345][346] W. Wolberg et al.
National Survey on Drug Use and Health Large scale survey on health and drug use in the United States. None. 55,268 Text Classification, regression 2012 [347] United States Department of Health and Human Services
Lung Cancer Dataset Lung cancer dataset without attribute definitions 56 features are given for each case 32 Text Classification 1992 [348][349] Z. Hong et al.
Arrhythmia Dataset Data for a group of patients, of which some have cardiac arrhythmia. 276 features for each instance. 452 Text Classification 1998 [350][351] H. Altay et al.
Diabetes 130-US hospitals for years 1999–2008 Dataset 9 years of readmission data across 130 US hospitals for patients with diabetes. Many features of each readmission are given. 100,000 Text Classification, clustering 2014 [352][353] J. Clore et al.
Diabetic Retinopathy Debrecen Dataset Features extracted from images of eyes with and without diabetic retinopathy. Features extracted and conditions diagnosed. 1151 Text Classification 2014 [354][355] B. Antal et al.
Diabetic Retinopathy Messidor Dataset Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR) Features retinopathy grade and risk of macular edema 1200 Images,Text Classification, Segmentation 2008 [356][357] Messidor Project
Liver Disorders Dataset Data for people with liver disorders. Seven biological features given for each patient. 345 Text Classification 1990 [358][359] Bupa Medical Research Ltd.
Thyroid Disease Dataset 10 databases of thyroid disease patient data. None. 7200 Text Classification 1987 [360][361] R. Quinlan
Mesothelioma Dataset Mesothelioma patient data. Large number of features, including asbestos exposure, are given. 324 Text Classification 2016 [362][363] A. Tanrikulu et al.
Parkinson's Vision-Based Pose Estimation Dataset 2D human pose estimates of Parkinson's patients performing a variety of tasks. Camera shake has been removed from trajectories. 134 Text Classification, regression 2017 [364][365][366] M. Li et al.
KEGG Metabolic Reaction Network (Undirected) Dataset Network of metabolic pathways. A reaction network and a relation network are given. Detailed features for each network node and pathway are given. 65,554 Text Classification, clustering, regression 2011 [367] M. Naeem et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Abalone Dataset Physical measurements of Abalone. Weather patterns and location are also given. None. 4177 Text Regression 1995 [368] Marine Research Laboratories – Taroona
Zoo Dataset Artificial dataset covering 7 classes of animals. Animals are classed into 7 categories and features are given for each. 101 Text Classification 1990 [369] R. Forsyth
Demospongiae Dataset Data about marine sponges. 503 sponges in the Demosponge class are described by various features. 503 Text Classification 2010 [370] E. Armengol et al.
Splice-junction Gene Sequences Dataset Primate splice-junction gene sequences (DNA) with associated imperfect domain theory. None. 3190 Text Classification 1992 [349] G. Towell et al.
Mice Protein Expression Dataset Expression levels of 77 proteins measured in the cerebral cortex of mice. None. 1080 Text Classification, Clustering 2015 [371][372] C. Higuera et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Forest Fires Dataset Forest fires and their properties. 13 features of each fire are extracted. 517 Text Regression 2008 [373][374] P. Cortez et al.
Iris Dataset Three types of iris plants are described by 4 different attributes. None. 150 Text Classification 1936 [375][376] R. Fisher
Plant Species Leaves Dataset Sixteen samples of leaf each of one-hundred plant species. Shape descriptor, fine-scale margin, and texture histograms are given. 1600 Text Classification 2012 [377][378] J. Cope et al.
Mushroom Dataset Mushroom attributes and classification. Many properties of each mushroom are given. 8124 Text Classification 1987 [379] J. Schlimmer
Soybean Dataset Database of diseased soybean plants. 35 features for each plant are given. Plants are classified into 19 categories. 307 Text Classification 1988 [380] R. Michalski et al.
Seeds Dataset Measurements of geometrical properties of kernels belonging to three different varieties of wheat. None. 210 Text Classification, clustering 2012 [381][382] Charytanowicz et al.
Covertype Dataset Data for predicting forest cover type strictly from cartographic variables. Many geographical features given. 581,012 Text Classification 1998 [383][384] J. Blackard et al.
Abscisic Acid Signaling Network Dataset Data for a plant signaling network. Goal is to determine set of rules that governs the network. None. 300 Text Causal-discovery 2008 [385] J. Jenkens et al.
Folio Dataset 20 photos of leaves for each of 32 species. None. 637 Images, text Classification, clustering 2015 [386][387] T. Munisami et al.
Oxford Flower Dataset 17 category dataset of flowers. Train/test splits, labeled images, 1360 Images, text Classification 2006 [137][388] M-E Nilsback et al.
Plant Seedlings Dataset 12 category dataset of plant seedlings. Labelled images, segmented images, 5544 Images Classification, detection 2017 [389] Giselsson et al.
Fruits 360 Dataset 81 Fruits: Apples (different varieties: Golden, Golden-Red, Granny Smith, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red), Cactus fruit, Cantaloupe (2 varieties), Carambula, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Clementine, Cocos, Dates, Granadilla, Grape (Pink, White, White2), Grapefruit (Pink, White), Guava, Huckleberry, Kiwi, Kaki, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine, Orange, Papaya, Passion fruit, Peach, Pepino, Pear (different varieties, Abate, Monster, Williams), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum, Pomegranate, Quince, Rambutan, Raspberry, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red), Walnut. 100x100 pixels, White background. 55244 Images (jpg) Classification 2017 [390][391] Mihai Oltean, Horea Muresan


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Ecoli Dataset Protein localization sites. Various features of the protein localizations sites are given. 336 Text Classification 1996 [392][393] K. Nakai et al.
MicroMass Dataset Identification of microorganisms from mass-spectrometry data. Various mass spectrometer features. 931 Text Classification 2013 [394][395] P. Mahe et al.
Yeast Dataset Predictions of Cellular localization sites of proteins. Eight features given per instance. 1484 Text Classification 1996 [396][397] K. Nakai et al.

Drug Discovery

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Tox21 Dataset Prediction of outcome of biological assays. Chemical descriptors of molecules are given. 12707 Text Classification 2016 [398] A. Mayr et al.

Anomaly data

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Numenta Anomaly Benchmark (NAB) Data are ordered, timestamped, single-valued metrics. All data files contain anomalies, unless otherwise noted. None 50+ files Comma separated values Anomaly detection 2016 (continually updated) [399] Numenta
On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF Anomaly detection 2016 (possibly updated with new datasets and/or results)


Campos et al.

Question Answering data

This section includes datasets that deals with structured data.

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
DBpedia Neural Question Answering (DBNQA) Dataset A large collection of Question to SPARQL specially design for Open Domain Neural Question Answering over DBpedia Knowledgebase. This dataset contains a large collection of Open Neural SPARQL Templates and instances for training Neural SPARQL Machines; it was pre-processed by semi-automatic annotation tools as well as by three SPARQL experts. 894,499 Question-query pairs Question Answering 2018 [401][402] Hartmann, Soru, and Marx et al.

Multivariate data

Datasets consisting of rows of observations and columns of attributes characterizing those observations. Typically used for regression analysis or classification but other types of algorithms can also be used. This section includes datasets that do not fit in the above categories.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Dow Jones Index Weekly data of stocks from the first and second quarters of 2011. Calculated values included such as percentage change and a lags. 750 Comma separated values Classification, regression, Time series 2014 [403][404] M. Brown et al.
Statlog (Australian Credit Approval) Credit card applications either accepted or rejected and attributes about the application. Attribute names are removed as well as identifying information. Factors have been relabeled. 690 Comma separated values Classification 1987 [405][406] R. Quinlan
eBay auction data Auction data from various objects over various length auctions Contains all bids, bidderID, bid times, and opening prices. ~ 550 Text Regression, classification 2012 [407][408] G. Shmueli et al.
Statlog (German Credit Data) Binary credit classification into "good" or "bad" with many features Various financial features of each person are given. 690 Text Classification 1994 [409] H. Hofmann
Bank Marketing Dataset Data from a large marketing campaign carried out by a large bank . Many attributes of the clients contacted are given. If the client subscribed to the bank is also given. 45,211 Text Classification 2012 [410][411] S. Moro et al.
Istanbul Stock Exchange Dataset Several stock indexes tracked for almost two years. None. 536 Text Classification, regression 2013 [412][413] O. Akbilgic
Default of Credit Card Clients Credit default data for Taiwanese creditors. Various features about each account are given. 30,000 Text Classification 2016 [414][415] I. Yeh


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Cloud DataSet Data about 1024 different clouds. Image features extracted. 1024 Text Classification, clustering 1989 [416] P. Collard
El Nino Dataset Oceanographic and surface meteorological readings taken from a series of buoys positioned throughout the equatorial Pacific. 12 weather attributes are measured at each buoy. 178080 Text Regression 1999 [417] Pacific Marine Environmental Laboratory
Greenhouse Gas Observing Network Dataset Time-series of greenhouse gas concentrations at 2921 grid cells in California created using simulations of the weather. None. 2921 Text Regression 2015 [418] D. Lucas
Atmospheric CO2 from Continuous Air Samples at Mauna Loa Observatory Continuous air samples in Hawaii, USA. 44 years of records. None. 44 years Text Regression 2001 [419] Mauna Loa Observatory
Ionosphere Dataset Radar data from the ionosphere. Task is to classify into good and bad radar returns. Many radar features given. 351 Text Classification 1989 [361][420] Johns Hopkins University
Ozone Level Detection Dataset Two ground ozone level datasets. Many features given, including weather conditions at time of measurement. 2536 Text Classification 2008 [421][422] K. Zhang et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Adult Dataset Census data from 1994 containing demographic features of adults and their income. Cleaned and anonymized. 48,842 Comma separated values Classification 1996 [423] United States Census Bureau
Census-Income (KDD) Weighted census data from the 1994 and 1995 Current Population Surveys. Split into training and test sets. 299,285 Comma separated values Classification 2000 [424][425] United States Census Bureau
IPUMS Census Database Census data from the Los Angeles and Long Beach areas. None 256,932 Text Classification, regression 1999 [426] IPUMS
US Census Data 1990 Partial data from 1990 US census. Results randomized and useful attributes selected. 2,458,285 Text Classification, regression 1990 [427] United States Census Bureau


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Bike Sharing Dataset Hourly and daily count of rental bikes in a large city. Many features, including weather, length of trip, etc., are given. 17,389 Text Regression 2013 [428][429] H. Fanaee-T
New York City Taxi Trip Data Trip data for yellow and green taxis in New York City. Gives pick up and drop off locations, fares, and other details of trips. 6 years Text Classification, clustering 2015 New York City Taxi and Limousine Commission
Taxi Service Trajectory ECML PKDD Trajectories of all taxis in a large city. Many features given, including start and stop points. 1,710,671 Text Clustering, causal-discovery 2015 [430][431] M. Ferreira et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Webpages from Common Crawl 2012 Large collection of webpages and how they are connected via hyperlinks None. 3.5B Text clustering, classification 2013 [432] V. Granville
Internet Advertisements Dataset Dataset for predicting if a given image is an advertisement or not. Features encode geometry of ads and phrases occurring in the URL. 3279 Text Classification 1998 [433][434] N. Kushmerick
Internet Usage Dataset General demographics of internet users. None. 10,104 Text Classification, clustering 1999 [435] D. Cook
URL Dataset 120 days of URL data from a large conference. Many features of each URL are given. 2,396,130 Text Classification 2009 [436][437] J. Ma
Phishing Websites Dataset Dataset of phishing websites. Many features of each site are given. 2456 Text Classification 2015 [438] R. Mustafa et al.
Online Retail Dataset Online transactions for a UK online retailer. Details of each transaction given. 541,909 Text Classification, clustering 2015 [439] D. Chen
Freebase Simple Topic Dump Freebase is an online effort to structure all human knowledge. Topics from Freebase have been extracted. large Text Classification, clustering 2011 [440][441] Freebase
Farm Ads Dataset The text of farm ads from websites. Binary approval or disapproval by content owners is given. SVMlight sparse vectors of text words in ads calculated. 4143 Text Classification 2011 [442][443] C. Masterharm et al.


Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Poker Hand Dataset 5 card hands from a standard 52 card deck. Attributes of each hand are given, including the Poker hands formed by the cards it contains. 1,025,010 Text Regression, classification 2007 [444] R. Cattral
Connect-4 Dataset Contains all legal 8-ply positions in the game of connect-4 in which neither player has won yet, and in which the next move is not forced. None. 67,557 Text Classification 1995 [445] J. Tromp
Chess (King-Rook vs. King) Dataset Endgame Database for White King and Rook against Black King. None. 28,056 Text Classification 1994 [446][447] M. Bain et al.
Chess (King-Rook vs. King-Pawn) Dataset King+Rook versus King+Pawn on a7. None. 3196 Text Classification 1989 [448] R. Holte
Tic-Tac-Toe Endgame Dataset Binary classification for win conditions in tic-tac-toe. None. 958 Text Classification 1991 [449] D. Aha

Other multivariate

Dataset Name Brief description Preprocessing Instances Format Default Task Created (updated) Reference Creator
Housing Data Set Median home values of Boston with associated home and neighborhood attributes. None. 506 Text Regression 1993 [450] D. Harrison et al.
The Getty Vocabularies structured terminology for art and other material culture, archival materials, visual surrogates, and bibliographic materials. None. large Text Classification 2015 [451] Getty Center
Yahoo! Front Page Today Module User Click Log User click log for news articles displayed in the Featured Tab of the Today Module on Yahoo! Front Page. Conjoint analysis with a bilinear model. 45,811,883 user visits Text Regression, clustering 2009 [452][453] Chu et al.
British Oceanographic Data Centre Biological, chemical, physical and geophysical data for oceans. 22K variables tracked. Various. 22K variables, many instances Text Regression, clustering 2015 [454] British Oceanographic Data Centre
Congressional Voting Records Dataset Voting data for all USA representatives on 16 issues. Beyond the raw voting data, various other features are provided. 435 Text Classification 1987 [455] J. Schlimmer
Entree Chicago Recommendation Dataset Record of user interactions with Entree Chicago recommendation system. Details of each users usage of the app are recorded in detail. 50,672 Text Regression, recommendation 2000 [456] R. Burke
Insurance Company Benchmark (COIL 2000) Information on customers of an insurance company. Many features of each customer and the services they use. 9,000 Text Regression, classification 2000 [457][458] P. van der Putten
Nursery Dataset Data from applicants to nursery schools. Data about applicant's family and various other factors included. 12,960 Text Classification 1997 [459][460] V. Rajkovic et al.
University Dataset Data describing attributed of a large number of universities. None. 285 Text Clustering, classification 1988 [461] S. Sounders et al.
Blood Transfusion Service Center Dataset Data from blood transfusion service center. Gives data on donors return rate, frequency, etc. None. 748 Text Classification 2008 [462][463] I. Yeh
Record Linkage Comparison Patterns Dataset Large dataset of records. Task is to link relevant records together. Blocking procedure applied to select only certain record pairs. 5,749,132 Text Classification 2011 [464][465] University of Mainz
Nomao Dataset Nomao collects data about places from many different sources. Task is to detect items that describe the same place. Duplicates labeled. 34,465 Text Classification 2012 [466][467] Nomao Labs
Movie Dataset Data for 10,000 movies. Several features for each movie are given. 10,000 Text Clustering, classification 1999 [468] G. Wiederhold
Open University Learning Analytics Dataset Information about students and their interactions with a virtual learning environment. None. ~ 30,000 Text Classification, clustering, regression 2015 [469][470] J. Kuzilek et al.
Mobile phone records Telecommunications activity and interactions Aggregation per geographical grid cells and every 15 minutes. large Text Classification, Clustering, Regression 2015 [471] G. Barlacchi et al.

Curated repositories of datasets

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

  • OpenML:[472] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
  • PMLB:[473] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Provides classification and regression datasets in a standardized format that are accessible through a Python API.

See also


  1. Wissner-Gross, A.. "Datasets Over Algorithms". Retrieved 8 January 2016. 
  2. Weiss, Gary M., and Foster Provost. "Learning when training data are costly: the effect of class distribution on tree induction." Journal of Artificial Intelligence Research (2003): 315–354.
  3. Turney, Peter. "Types of cost in inductive concept learning." (2000).
  4. Abney, Steven. Semisupervised learning for computational linguistics. CRC Press, 2007.
  5. Žliobaitė, Indrė, et al. "Active learning with evolving streaming data." Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011. 597–612.
  6. Phillips, P. Jonathon et al. (1998). "The FERET database and evaluation procedure for face-recognition algorithms". Image and Vision Computing 16 (5): 295–306. doi:10.1016/s0262-8856(97)00070-x. 
  7. Wiskott, Laurenz et al. (1997). "Face recognition by elastic bunch graph matching". Pattern Analysis and Machine Intelligence, IEEE Transactions on 19 (7): 775–779. doi:10.1109/34.598235. 
  8. Sim, Terence, Simon Baker, and Maan Bsat. "The CMU pose, illumination, and expression (PIE) database." Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on. IEEE, 2002.
  9. Schroff, Florian, et al. "Pose, illumination and expression invariant pairwise face-similarity measure via doppelgänger list comparison."Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.
  10. Livingstone & Russo (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. doi:10.1371/journal.pone.0196391
  11. doi:10.5281/zenodo.1188976
  12. Grgic, Mislav; Delac, Kresimir; Grgic, Sonja (2011). "SCface–surveillance cameras face database". Multimedia Tools and Applications 51 (3): 863–879. 
  13. Wallace, Roy, et al. "Inter-session variability modelling and joint factor analysis for face authentication." Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011.
  14. Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  15. Wolf, Lior, Tal Hassner, and Itay Maoz. "Face recognition in unconstrained videos with matched background similarity." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
  16. Shen, Jie, et al. "The first facial landmark tracking in-the-wild challenge: Benchmark and results." 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2015.
  17. de Almeida Freitas, Fernando, et al. "Grammatical Facial Expressions Recognition with Machine Learning." FLAIRS Conference. 2014.
  18. Mitchell, Tom M. "Machine learning. WCB." (1997).
  19. Xiaofeng He and Partha Niyogi. Locality Preserving Projections. NIPS. 2003.
  20. Georghiades, A. "Yale face database." Center for computational Vision and Control at Yale University, 2 (1997).
  21. Nguyen, Duy et al. (2006). "Real-time face detection and lip feature extraction using field-programmable gate arrays". Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 36 (4): 902–912. doi:10.1109/tsmcb.2005.862728. 
  22. Kanade, Takeo, Jeffrey F. Cohn, and Yingli Tian. "Comprehensive database for facial expression analysis." Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, 2000.
  23. Zeng, Zhihong et al. (2009). "A survey of affect recognition methods: Audio, visual, and spontaneous expressions". Pattern Analysis and Machine Intelligence, IEEE Transactions on 31 (1): 39–58. doi:10.1109/tpami.2008.52. PMID 19029545. 
  24. Ng, Hong-Wei, and Stefan Winkler. "A data-driven approach to cleaning large face datasets." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014.
  25. RoyChowdhury, Aruni; Lin, Tsung-Yu; Maji, Subhransu; Learned-Miller, Erik (2015). "One-to-many face recognition with bilinear CNNs". arXiv:1506.01342 [cs.CV].
  26. Jesorsky, Oliver, Klaus J. Kirchberg, and Robert W. Frischholz. "Robust face detection using the hausdorff distance." Audio-and video-based biometric person authentication. Springer Berlin Heidelberg, 2001.
  27. Huang, Gary B., et al. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Vol. 1. No. 2. Technical Report 07-49, University of Massachusetts, Amherst, 2007.
  28. Bhatt, Rajen B., et al. "Efficient skin region segmentation using low complexity fuzzy decision tree model." India Conference (INDICON), 2009 Annual IEEE. IEEE, 2009.
  29. Lingala, Mounika et al. (2014). "Fuzzy logic color detection: Blue areas in melanoma dermoscopy images". Computerized Medical Imaging and Graphics 38 (5): 403–410. doi:10.1016/j.compmedimag.2014.03.007. PMID 24786720. 
  30. Maes, Chris, et al. "Feature detection on 3D face surfaces for pose normalisation and recognition." Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, 2010.
  31. Savran, Arman, et al. "Bosphorus database for 3D face analysis." Biometrics and Identity Management. Springer Berlin Heidelberg, 2008. 47–56.
  32. Heseltine, Thomas, Nick Pears, and Jim Austin. "Three-dimensional face recognition: An eigensurface approach." Image Processing, 2004. ICIP'04. 2004 International Conference on. Vol. 2. IEEE, 2004.
  33. Ge, Yun et al. (2011). "3D Novel Face Sample Modeling for Face Recognition". Journal of Multimedia 6 (5): 467–475. doi:10.4304/jmm.6.5.467-475. 
  34. Wang, Yueming; Liu, Jianzhuang; Tang, Xiaoou (2010). "Robust 3D face recognition by local shape difference boosting". Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (10): 1858–1870. doi:10.1109/tpami.2009.200. PMID 20724762. 
  35. Zhong, Cheng, Zhenan Sun, and Tieniu Tan. "Robust 3D face recognition using learned visual codebook." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.
  36. Zhao, G., Huang, X., Taini, M., Li, S. Z., & Pietikäinen, M. (2011). Facial expression recognition from near-infrared videos. Image and Vision Computing, 29(9), 607–619.
  37. Soyel, Hamit, and Hasan Demirel. "Facial expression recognition using 3D facial feature distances." Image Analysis and Recognition. Springer Berlin Heidelberg, 2007. 831–838.
  38. Bowyer, Kevin W.; Chang, Kyong; Flynn, Patrick (2006). "A survey of approaches and challenges in 3D and multi-modal 3D+ 2D face recognition". Computer Vision and Image Understanding 101 (1): 1–15. doi:10.1016/j.cviu.2005.05.005. 
  39. Tan, Xiaoyang; Triggs, Bill (2010). "Enhanced local texture feature sets for face recognition under difficult lighting conditions". Image Processing, IEEE Transactions on 19 (6): 1635–1650. doi:10.1109/tip.2010.2042645. PMID 20172829. Bibcode2010ITIP...19.1635T. 
  40. Mousavi, Mir Hashem, Karim Faez, and Amin Asghari. "Three dimensional face recognition using SVM classifier." Computer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference on. IEEE, 2008.
  41. Amberg, Brian, Reinhard Knothe, and Thomas Vetter. "Expression invariant 3D face recognition with a morphable model." Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on. IEEE, 2008.
  42. İrfanoğlu, M. O., Berk Gökberk, and Lale Akarun. "3D shape-based face recognition using automatically registered facial surfaces." Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. Vol. 4. IEEE, 2004.
  43. Beumier, Charles; Acheroy, Marc (2001). "Face verification from 3D and grey level clues". Pattern Recognition Letters 22 (12): 1321–1329. doi:10.1016/s0167-8655(01)00077-0. 
  44. Afifi, Mahmoud; Abdelhamed, Abdelrahman (2017-06-13). "AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces". arXiv:1706.04277 [cs.CV].
  45. "SoF dataset" (in en-US). 
  46. "IMDB-WIKI" (in en-US). 
  47. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. "HMDB: A Large Video Database for Human Motion Recognition." ICCV, 2011.
  48. Patron-Perez, A.; Marszalek, M.; Reid, I.; Zisserman, A. (2012). "Structured learning of human interactions in TV shows". IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (12): 2441–2453. doi:10.1109/tpami.2012.24. PMID 23079467. 
  49. Ryoo, M. S., & Aggarwal, J. K. (September 2009). Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In Computer vision, 2009 ieee 12th international conference on (pp. 1593–1600). IEEE.
  50. Xia, L., Chen, C. C., & Aggarwal, J. K. (June 2012). View invariant human action recognition using histograms of 3d joints. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on (pp. 20–27). IEEE.
  51. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., & Samaras, D. (June 2012). Two-person interaction detection using body-pose features and multiple instance learning. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on (pp. 28–35). IEEE.
  52. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., & Bajcsy, R. (January 2013). Berkeley MHAD: A comprehensive multimodal human action database. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on (pp. 53–60). IEEE.
  53. Soomro, Khurram; Amir Roshan Zamir; Shah, Mubarak (2012). "UCF101: A Dataset of 101 Human Actions Classes from Videos in the Wild". arXiv:1212.0402 [cs.CV].
  54. Karpathy, Andrej, et al. "Large-scale video classification with convolutional neural networks." Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2014.
  55. Jiang, Y. G., et al. "THUMOS challenge: Action recognition with a large number of classes." ICCV Workshop on Action Recognition with a Large Number of Classes, 2013.
  56. Simonyan, Karen, and Andrew Zisserman. "Two-stream convolutional networks for action recognition in videos." Advances in Neural Information Processing Systems. 2014.
  57. Caba Heilbron, Fabian, et al. "Activitynet: A large-scale video benchmark for human activity understanding." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
  58. Sadoughi, N., Liu, Y., & Busso, C. (May 2015). MSP-AVATAR corpus: Motion capture recordings to study the role of discourse functions in the design of intelligent virtual agents. In Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on (Vol. 7, pp. 1–6). IEEE.
  59. Sheerman-Chase, T., Ong, E. J., & Bowden, R. (November 2011). Cultural factors in the regression of non-verbal communication perception. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on (pp. 1242–1249). IEEE.
  60. Stoian, Andrei; Ferecatu, Marin; Benois-Pineau, Jenny; Crucianu, Michel (2016). "Fast Action Localization in Large-Scale Video Archives". IEEE Transactions on Circuits and Systems for Video Technology 26 (10): 1917–1930. doi:10.1109/TCSVT.2015.2475835. 
  61. Krishna, Ranjay; Zhu, Yuke; Groth, Oliver; Johnson, Justin; Hata, Kenji; Kravitz, Joshua; Chen, Stephanie; Kalantidis, Yannis et al. (2017). "Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations". International Journal of Computer Vision 123: 32–73. doi:10.1007/s11263-016-0981-7. 
  62. Pont-Tuset, Jordi; Perazzi, Federico; Caelles, Sergi; Arbeláez, Pablo; Sorkine-Hornung, Alex; Luc Van Gool (2017). "The 2017 DAVIS Challenge on Video Object Segmentation". arXiv:1704.00675 [cs.CV].
  63. Perazzi, Federico; Pont-Tuset, Jordi; McWilliams, Brian; Van Gool, Luc; Gross, Markus; Sorkine-Hornung, Alex (2016). A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation. 
  64. Hodan, T., et al. "T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects." Winter Conference on Applications of Computer Vision (WACV) 2017.
  65. Karayev, S., et al. "A category-level 3-D object dataset: putting the Kinect to work." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2011.
  66. Tighe, Joseph, and Svetlana Lazebnik. "Superparsing: scalable nonparametric image parsing with superpixels." Computer Vision–ECCV 2010. Springer Berlin Heidelberg, 2010. 352–365.
  67. Arbelaez, P.; Maire, M; Fowlkes, C; Malik, J (May 2011). "Contour Detection and Hierarchical Image Segmentation". Ieee Tpam 33 (5): 898–916. doi:10.1109/tpami.2010.161. PMID 20733228. Retrieved 27 February 2016. 
  68. Lin, Tsung-Yi, et al. "Microsoft coco: Common objects in context." Computer Vision–ECCV 2014. Springer International Publishing, 2014. 740–755.
  69. Russakovsky, Olga et al. (2015). "Imagenet large scale visual recognition challenge". International Journal of Computer Vision 115 (3): 211–252. doi:10.1007/s11263-015-0816-y. 
  70. Xiao, Jianxiong, et al. "Sun database: Large-scale scene recognition from abbey to zoo." Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. IEEE, 2010.
  71. Donahue, Jeff; Jia, Yangqing; Vinyals, Oriol; Hoffman, Judy; Zhang, Ning; Tzeng, Eric; Darrell, Trevor (2013). "DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition". arXiv:1310.1531 [cs.CV].
  72. Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database."Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
  73. 73.0 73.1 73.2 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
  74. Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev et al. (11 April 2015). "ImageNet Large Scale Visual Recognition Challenge". International Journal of Computer Vision 115 (3): 211–252. doi:10.1007/s11263-015-0816-y. 
  75. Ivan Krasin, Tom Duerig, Neil Alldrin, Andreas Veit, Sami Abu-El-Haija, Serge Belongie, David Cai, Zheyun Feng, Vittorio Ferrari, Victor Gomes, Abhinav Gupta, Dhyanesh Narayanan, Chen Sun, Gal Chechik, Kevin Murphy. "OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2017. Available from"
  76. Vyas, Apoorv, et al. "Commercial Block Detection in Broadcast News Videos." Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing. ACM, 2014.
  77. Hauptmann, Alexander G., and Michael J. Witbrock. "Story segmentation and detection of commercials in broadcast news video." Research and Technology Advances in Digital Libraries, 1998. ADL 98. Proceedings. IEEE International Forum on. IEEE, 1998.
  78. Tung, Anthony KH, Xin Xu, and Beng Chin Ooi. "Curler: finding and visualizing nonlinear correlation clusters." Proceedings of the 2005 ACM SIGMOD international conference on Management of data. ACM, 2005.
  79. Jarrett, Kevin, et al. "What is the best multi-stage architecture for object recognition?." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009.
  80. Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce. "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories."Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.
  81. Griffin, G., A. Holub, and P. Perona. Caltech-256 object category dataset California Inst. Technol., Tech. Rep. 7694, 2007 [Online]. Available: , 2007.
  82. Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern information retrieval. Vol. 463. New York: ACM press, 1999.
  83. Fu, Xiping, et al. "NOKMeans: Non-Orthogonal K-means Hashing." Computer Vision—ACCV 2014. Springer International Publishing, 2014. 162–177.
  84. Heitz, Geremy et al. (2009). "Shape-based object localization for descriptive classification". International Journal of Computer Vision 84 (1): 40–62. doi:10.1007/s11263-009-0228-y. 
  85. M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, "The Cityscapes Dataset." In CVPR Workshop on The Future of Datasets in Vision, 2015.
  86. Everingham, Mark et al. (2010). "The pascal visual object classes (voc) challenge". International Journal of Computer Vision 88 (2): 303–338. doi:10.1007/s11263-009-0275-4. 
  87. Felzenszwalb, Pedro F. et al. (2010). "Object detection with discriminatively trained part-based models". Pattern Analysis and Machine Intelligence, IEEE Transactions on 32 (9): 1627–1645. doi:10.1109/tpami.2009.167. PMID 20634557. 
  88. 88.0 88.1 Gong, Yunchao, and Svetlana Lazebnik. "Iterative quantization: A procrustean approach to learning binary codes." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
  89. fashion-mnist: A MNIST-like fashion product database. Benchmark :point_right:, Zalando Research, 2017-10-07,, retrieved 2017-10-07 
  90. "notMNIST dataset". 2011-09-08. Retrieved 2017-10-13. 
  91. Houben, Sebastian, et al. "Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
  92. Mathias, Mayeul, et al. "Traffic sign recognition—How far are we from the solution?." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
  93. Geiger, Andreas, Philip Lenz, and Raquel Urtasun. "Are we ready for autonomous driving? the kitti vision benchmark suite." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  94. Sturm, Jürgen, et al. "A benchmark for the evaluation of RGB-D SLAM systems." Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 2012.
  95. Chaladze, G., Kalatozishvili, L. (2017). Linnaeus 5 Retrieved 13 November 2017, from
  96. Kragh, Mikkel F. et al. (2017). "FieldSAFE – Dataset for Obstacle Detection in Agriculture". Sensors 17 (11): 2579. doi:10.3390/s17112579. PMID 29120383. PMC 5713196. 
  97. Afifi, Mahmoud (2017-11-12). "Gender recognition and biometric identification using a large dataset of hand images". arXiv:1711.04322 [cs.CV].
  98. Lomonaco, Vincenzo; Maltoni, Davide (2017-10-18). "CORe50: a New Dataset and Benchmark for Continuous Object Recognition". arXiv:1705.03550 [cs.CV].
  99. Botta, M., A. Giordana, and L. Saitta. "Learning fuzzy concept definitions." Fuzzy Systems, 1993., Second IEEE International Conference on. IEEE, 1993.
  100. Frey, Peter W.; Slate, David J. (1991). "Letter recognition using Holland-style adaptive classifiers". Machine Learning 6 (2): 161–182. doi:10.1007/bf00114162. 
  101. Peltonen, Jaakko; Klami, Arto; Kaski, Samuel (2004). "Improved learning of Riemannian metrics for exploratory analysis". Neural Networks 17 (8): 1087–1100. doi:10.1016/j.neunet.2004.06.008. PMID 15555853. 
  102. Williams, Ben H., Marc Toussaint, and Amos J. Storkey. Extracting motion primitives from natural handwriting data. Springer Berlin Heidelberg, 2006.
  103. Meier, Franziska, et al. "Movement segmentation using a primitive library."Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011.
  104. T. E. de Campos, B. R. Babu and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal, February 2009
  105. Llorens, David, et al. "The UJIpenchars Database: a Pen-Based Database of Isolated Handwritten Characters." LREC. 2008.
  106. Calderara, Simone; Prati, Andrea; Cucchiara, Rita (2011). "Mixtures of von mises distributions for people trajectory shape analysis". Circuits and Systems for Video Technology, IEEE Transactions on 21 (4): 457–471. doi:10.1109/tcsvt.2011.2125550. 
  107. Guyon, Isabelle, et al. "Result analysis of the nips 2003 feature selection challenge." Advances in neural information processing systems. 2004.
  108. LeCun, Yann et al. (1998). "Gradient-based learning applied to document recognition". Proceedings of the IEEE 86 (11): 2278–2324. doi:10.1109/5.726791. 
  109. Kussul, Ernst, and Tatiana Baidyk. "Improved method of handwritten digit recognition tested on MNIST database." Image and Vision Computing22.12 (2004): 971-981.
  110. Xu, Lei; Krzyżak, Adam; Suen, Ching Y. (1992). "Methods of combining multiple classifiers and their applications to handwriting recognition". Systems, Man and Cybernetics, IEEE Transactions on 22 (3): 418–435. doi:10.1109/21.155943. 
  111. Alimoglu, Fevzi, et al. "Combining multiple classifiers for pen-based handwritten digit recognition." (1996).
  112. Tang, E. Ke et al. (2005). "Linear dimensionality reduction using relevance weighted LDA". Pattern Recognition 38 (4): 485–493. doi:10.1016/j.patcog.2004.09.005. 
  113. Hong, Yi, et al. "Learning a mixture of sparse distance metrics for classification and dimensionality reduction." Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.
  114. Thoma, Martin (2017). "The HASYv2 dataset". arXiv:1701.08380 [cs.CV].
  115. Yuan, Jiangye; Gleason, Shaun S.; Cheriyadat, Anil M. (2013). "Systematic benchmarking of aerial image segmentation". Geoscience and Remote Sensing Letters, IEEE 10 (6): 1527–1531. doi:10.1109/lgrs.2013.2261453. Bibcode2013IGRSL..10.1527Y. 
  116. Vatsavai, Ranga Raju. "Object based image classification: state of the art and computational challenges." Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. ACM, 2013.
  117. Butenuth, Matthias, et al. "Integrating pedestrian simulation, tracking and event detection for crowd analysis." Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on. IEEE, 2011.
  118. Fradi, Hajer, and Jean-Luc Dugelay. "Low level crowd analysis using frame-wise normalized feature for people counting." Information Forensics and Security (WIFS), 2012 IEEE International Workshop on. IEEE, 2012.
  119. Johnson, Brian Alan, Ryutaro Tateishi, and Nguyen Thanh Hoan. "A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees." International journal of remote sensing34.20 (2013): 6969–6982.
  120. Mohd Pozi, Muhammad Syafiq, et al. "A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification." Remote Sensing Letters6.7 (2015): 568–577.
  121. Johnson, Brian; Tateishi, Ryutaro; Xie, Zhixiao (2012). "Using geographically weighted variables for image classification". Remote Sensing Letters 3 (6): 491–499. doi:10.1080/01431161.2011.629637. 
  122. Chatterjee, Sankhadeep, et al. "Forest Type Classification: A Hybrid NN-GA Model Based Approach." Information Systems Design and Intelligent Applications. Springer India, 2016. 227-236.
  123. Diegert, Carl. "A combinatorial method for tracing objects using semantics of their shape." Applied Imagery Pattern Recognition Workshop (AIPR), 2010 IEEE 39th. IEEE, 2010.
  124. Razakarivony, Sebastien, and Frédéric Jurie. "Small target detection combining foreground and background manifolds." IAPR International Conference on Machine Vision Applications. 2013.
  125. "SpaceNet". 
  126. Etten, Adam Van (2017-01-05). "Getting Started With SpaceNet Data". 
  127. Vakalopoulou, M.; Bus, N.; Karantzalosa, K.; Paragios, N. (July 2017). Integrating edge/boundary priors with classification scores for building detection in very high resolution data. 3309–3312. doi:10.1109/IGARSS.2017.8127705. ISBN 978-1-5090-4951-6. 
  128. Yang, Yi; Newsam, Shawn (2010). "Bag-of-visual-words and spatial extensions for land-use classification". Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '10 (New York, New York, USA: ACM Press). doi:10.1145/1869790.1869829. ISBN 9781450304283. 
  129. 129.0 129.1 Basu, Saikat; Ganguly, Sangram; Mukhopadhyay, Supratik; DiBiano, Robert; Karki, Manohar; Nemani, Ramakrishna (2015-11-03). DeepSat: a learning framework for satellite imagery. ACM. pp. 37. doi:10.1145/2820783.2820816. ISBN 9781450339674. 
  131. Rohrbach, Marcus, et al. "A database for fine grained activity detection of cooking activities."Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  132. Kuehne, Hilde, Ali Arslan, and Thomas Serre. "The language of actions: Recovering the syntax and semantics of goal-directed human activities."Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
  133. Sviatoslav, Voloshynovskiy, et al. "Towards Reproducible results in authentication based on physical non-cloneable functions: The Forensic Authentication Microstructure Optical Set (FAMOS)."Proc. Proceedings of IEEE International Workshop on Information Forensics and Security. 2012.
  134. Olga, Taran and Shideh, Rezaeifar, et al. "PharmaPack: mobile fine-grained recognition of pharma packages."Proc. European Signal Processing Conference (EUSIPCO). 2017.
  135. Khosla, Aditya, et al. "Novel dataset for fine-grained image categorization: Stanford dogs."Proc. CVPR Workshop on Fine-Grained Visual Categorization (FGVC). 2011.
  136. 136.0 136.1 Parkhi, Omkar M., et al. "Cats and dogs."Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  137. 137.0 137.1 Razavian, Ali, et al. "CNN features off-the-shelf: an astounding baseline for recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014.
  138. Ortega, Michael et al. (1998). "Supporting ranked boolean similarity queries in MARS". Knowledge and Data Engineering, IEEE Transactions on 10 (6): 905–925. doi:10.1109/69.738357. 
  139. He, Xuming, Richard S. Zemel, and Miguel Á. Carreira-Perpiñán. "Multiscale conditional random fields for image labeling." Computer vision and pattern recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE computer society conference on. Vol. 2. IEEE, 2004.
  140. Deneke, Tewodros, et al. "Video transcoding time prediction for proactive load balancing." Multimedia and Expo (ICME), 2014 IEEE International Conference on. IEEE, 2014.
  141. Ting-Hao (Kenneth) Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, Lucy Vanderwende, Michel Galley, Margaret Mitchell (13 April 2016). "Visual Storytelling". arXiv:1604.03968 [cs.CL].CS1 maint: multiple names: authors list (link)
  142. Wah, Catherine, et al. "The caltech-ucsd birds-200-2011 dataset." (2011).
  143. Duan, Kun, et al. "Discovering localized attributes for fine-grained recognition." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
  144. "YouTube-8M Dataset". Retrieved 1 October 2016. 
  145. Abu-El-Haija, Sami; Kothari, Nisarg; Lee, Joonseok; Natsev, Paul; Toderici, George; Varadarajan, Balakrishnan; Vijayanarasimhan, Sudheendra (27 September 2016). "YouTube-8M: A Large-Scale Video Classification Benchmark". arXiv:1609.08675 [cs.CV].
  146. "YFCC100M Dataset". Yahoo-ICSI-LLNL. Retrieved 1 June 2017. 
  147. Bart Thomee; David A Shamma; Gerald Friedland; Benjamin Elizalde; Karl Ni; Douglas Poland; Damian Borth; Li-Jia Li (25 April 2016). "Yfcc100m: The new data in multimedia research". Communications of the ACM 59 (2): 64–73. doi:10.1145/2812802. 
  148. Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, "LIRIS-ACCEDE: A Video Database for Affective Content Analysis,” in IEEE Transactions on Affective Computing, 2015.
  149. Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, "Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos," in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015.
  150. M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, "The mediaeval 2015 affective impact of movies task," in MediaEval 2015 Workshop, 2015.
  151. S. Johnson and M. Everingham, "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation", in Proceedings of the 21st British Machine Vision Conference (BMVC2010)
  152. S. Johnson and M. Everingham, "Learning Effective Human Pose Estimation from Inaccurate Annotation", In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2011)
  153. Afifi, Mahmoud; Hussain, Khaled F. (2017-11-02). "The Achievement of Higher Flexibility in Multiple Choice-based Tests Using Image Classification Techniques". arXiv:1711.00972 [cs.CV].
  154. "MCQ Dataset" (in en-US). 
  155. Taj-Eddin, I. A. T. F.; Afifi, M.; Korashy, M.; Hamdy, D.; Nasser, M.; Derbaz, S. (July 2016). A new compression technique for surveillance videos: Evaluation using new dataset. 159–164. doi:10.1109/DICTAP.2016.7544020. ISBN 978-1-4673-9609-7. 
  156. Taj-Eddin, Islam A. T. F.; Afifi, Mahmoud; Korashy, Mostafa; Ahmed, Ali H.; Ng, Yoke Cheng; Hernandez, Evelyng; Abdel-Latif, Salma M. (November 2017). "Can we see photosynthesis? Magnifying the tiny color changes of plant green leaves using Eulerian video magnification". Journal of Electronic Imaging 26 (6): 060501. doi:10.1117/1.jei.26.6.060501. ISSN 1017-9909. Bibcode2017JEI....26f0501T. 
  157. McAuley, Julian, et al. "Image-based recommendations on styles and substitutes." Proceedings of the 38th international ACM SIGIR conference on Research and development in information retrieval. ACM, 2015
  158. Ganesan, Kavita; Zhai, Chengxiang (2012). "Opinion-based entity ranking". Information Retrieval 15 (2): 116–150. doi:10.1007/s10791-011-9174-8. 
  159. Lv, Yuanhua, Dimitrios Lymberopoulos, and Qiang Wu. "An exploration of ranking heuristics in mobile local search." Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 2012.
  160. Harper, F. Maxwell; Konstan, Joseph A. (2015). "The MovieLens Datasets: History and Context". ACM Transactions on Interactive Intelligent Systems (TiiS) 5 (4): 19. 
  161. Koenigstein, Noam, Gideon Dror, and Yehuda Koren. "Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011.
  162. McFee, Brian, et al. "The million song dataset challenge." Proceedings of the 21st international conference companion on World Wide Web. ACM, 2012.
  163. Bohanec, Marko, and Vladislav Rajkovic. "Knowledge acquisition and explanation for multi-attribute decision making." 8th Intl Workshop on Expert Systems and their Applications. 1988.
  164. Tan, Peter J., and David L. Dowe. "MML inference of decision graphs with multi-way joins." Australian Joint Conference on Artificial Intelligence. 2002.
  165. "Quantifying comedy on YouTube: why the number of o's in your LOL matter". 
  166. Kim, Byung Joo. "A Classifier for Big Data."Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2012. 505–512.
  167. Pérezgonzález, Jose D.; Gilbey, Andrew (2011). "Predicting Skytrax airport rankings from customer reviews". Journal of Airport Management 5 (4): 335–339. 
  168. Loh, Wei-Yin, and Yu-Shan Shih. "Split selection methods for classification trees." Statistica sinica(1997): 815–840.
  169. Lim, Tjen-Sien; Loh, Wei-Yin; Shih, Yu-Shan (2000). "A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms". Machine Learning 40 (3): 203–228. doi:10.1023/a:1007608224229. 
  170. Dermouche, Mohamed, et al. "A Joint Model for Topic-Sentiment Evolution over Time." Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 2014.
  171. Rose, Tony; Stevenson, Mark; Whitehead, Miles (2002). "The Reuters Corpus Volume 1-from Yesterday's News to Tomorrow's Language Resources". LREC 2. 
  172. Amini, Massih, Nicolas Usunier, and Cyril Goutte. "Learning from multiple partially observed views-an application to multilingual text categorization."Advances in neural information processing systems. 2009.
  173. Liu, Ming, et al. "VRCA: a clustering algorithm for massive amount of texts."Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 2015.
  174. Al-Harbi, S, Almuhareb, A, Al-Thubaity, A, Khorsheed, M. S. and Al-Rajeh, A (2008) Automatic Arabic Text Classification. In, Proceedings of the 9th International Conference on the Statistical Analysis of Textual Data, Lyon, France
  181. Klimt, Bryan, and Yiming Yang. "Introducing the Enron Corpus." CEAS. 2004.
  182. Kossinets, Gueorgi, Jon Kleinberg, and Duncan Watts. "The structure of information pathways in a social communication network." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008.
  183. Androutsopoulos, Ion; Koutsias, John; Chandrinos, Konstantinos V.; Paliouras, George; Spyropoulos, Constantine D. (2000). G. Potamias, V. Moustakis and M. van Someren. ed. "An evaluation of Naive Bayesian anti-spam filtering". Proceedings of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning, Barcelona, Spain 11: 9–17. Bibcode2000cs........6013A. 
  184. Bratko, Andrej et al. (2006). "Spam filtering using statistical data compression models". The Journal of Machine Learning Research 7: 2673–2698. 
  185. Almeida, Tiago A., José María G. Hidalgo, and Akebo Yamakami. "Contributions to the study of SMS spam filtering: new collection and results."Proceedings of the 11th ACM symposium on Document engineering. ACM, 2011.
  186. Delany; Jane, Sarah; Buckley, Mark; Greene, Derek (2012). "SMS spam filtering: methods and data". Expert Systems with Applications 39 (10): 9899–9908. doi:10.1016/j.eswa.2012.02.053. 
  187. Joachims, Thorsten. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. No. CMU-CS-96-118. Carnegie-mellon univ pittsburgh pa dept of computer science, 1996.
  188. Dimitrakakis, Christos, and Samy Bengio. Online Policy Adaptation for Ensemble Algorithms. No. EPFL-REPORT-82788. IDIAP, 2002.
  189. Dooms, S. et al. "Movietweetings: a movie rating dataset collected from twitter, 2013. Available from"
  190. RoyChowdhury, Aruni; Lin, Tsung-Yu; Maji, Subhransu; Learned-Miller, Erik (2017). "Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval". arXiv:1703.06618 [cs.CV].
  191. "huyt16/Twitter100k" (in en). 
  192. Go, Alec; Bhayani, Richa; Huang, Lei (2009). "Twitter sentiment classification using distant supervision". CS224N Project Report, Stanford 1: 12. 
  193. Chikersal, Prerna, Soujanya Poria, and Erik Cambria. "SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning." Proceedings of the International Workshop on Semantic Evaluation, SemEval. 2015.
  194. Zafarani, Reza, and Huan Liu. "Social computing data repository at ASU." School of Computing, Informatics and Decision Systems Engineering, Arizona State University (2009).
  195. Bisgin, Halil, Nitin Agarwal, and Xiaowei Xu. "Investigating homophily in online social networks." Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on. Vol. 1. IEEE, 2010.
  196. McAuley, Julian J.; Leskovec, Jure. "Learning to Discover Social Circles in Ego Networks". NIPS 2012: 2012. 
  197. Šubelj, Lovro; Fiala, Dalibor; Bajec, Marko (2014). "Network-based statistical comparison of citation topology of bibliographic databases". Scientific Reports 4 (6496): 6496. doi:10.1038/srep06496. PMID 25263231. Bibcode2014NatSR...4E6496S. 
  198. Abdulla, N., et al. "Arabic sentiment analysis: Corpus-based and lexicon-based." Proceedings of the IEEE conference on Applied Electrical Engineering and Computing Technologies (AEECT). 2013.
  199. Abooraig, Raddad, et al. "On the automatic categorization of arabic articles based on their political orientation." Third International Conference on Informatics Engineering and Information Science (ICIEIS2014). 2014.
  200. Kawala, François, et al. "Prédictions d'activité dans les réseaux sociaux en ligne." 4ième conférence sur les modèles et l'analyse des réseaux: Approches mathématiques et informatiques. 2013.
  201. Sabharwal, Ashish; Samulowitz, Horst; Tesauro, Gerald (2015). "Selecting Near-Optimal Learners via Incremental Data Allocation". arXiv:1601.00024 [cs.LG].
  202. Xu et al. "SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)" Proceedings of the 9th International Workshop on Semantic Evaluation. 2015.
  203. Xu et al. "Extracting Lexically Divergent Paraphrases from Twitter" Transactions of the Association for Computational (TACL). 2014.
  204. Middleton, Stuart E; Middleton, Lee; Modafferi, Stefano (2014). "Real-Time Crisis Mapping of Natural Disasters Using Social Media". Ieee Intelligent Systems 29 (2): 9–17. doi:10.1109/MIS.2013.126. 
  205. "geoparsepy". 2016.  Python PyPI library
  206. Forsyth, E., Lin, J., & Martell, C. (2008, June 25). The NPS Chat Corpus. Retrieved from
  207. Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Meg Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan, A Neural Network Approach to Context-Sensitive Generation of Conversational Responses, Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL-HLT 2015), June 2015.
  208. Shaoul, C. & Westbury C. (2013) A reduced redundancy USENET corpus (2005-2011) Edmonton, AB: University of Alberta (downloaded from
  209. KAN, M. (2011, January). NUS Short Message Service (SMS) Corpus. Retrieved from
  210. Stuck_In_the_Matrix. (2015, July 3). I have every publicly available Reddit comment for research. ~ 1.7 billion comments @ 250 GB compressed. Any interest in this? [Original post]. Message posted to
  211. Ryan Lowe, Nissan Pow, Iulian V. Serban and Joelle Pineau, "The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructure Multi-Turn Dialogue Systems", SIGDial 2015.
  212. K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification", 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 364-371. doi: 10.1109/ICMLA.2017.0-134
  213. K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "Web of Science Dataset", doi: 10.17632/9rw3vkcfy4.6
  214. Galgani, Filippo, Paul Compton, and Achim Hoffmann. "Combining different summarization techniques for legal text." Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data. Association for Computational Linguistics, 2012.
  215. Nagwani, N. K. (2015). "Summarizing large text collection using topic modeling and clustering based on MapReduce framework". Journal of Big Data 2 (1): 1–18. doi:10.1186/s40537-015-0020-5. 
  216. Schler, Jonathan et al. (2006). "Effects of Age and Gender on Blogging". AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs 6. 
  217. Anand, Pranav, et al. "Believe Me-We Can Do This! Annotating Persuasive Acts in Blog Text."Computational Models of Natural Argument. 2011.
  218. Traud, Amanda L., Peter J. Mucha, and Mason A. Porter. "Social structure of Facebook networks." Physica A: Statistical Mechanics and its Applications391.16 (2012): 4165–4180.
  219. Richard, Emile; Savalle, Pierre-Andre; Vayatis, Nicolas (2012). "Estimation of Simultaneously Sparse and Low Rank Matrices". arXiv:1206.6474 [cs.DS].
  220. Richardson, Matthew; Burges, Christopher JC; Renshaw, Erin (2013). "MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text". EMNLP 1. 
  221. Weston, Jason; Bordes, Antoine; Chopra, Sumit; Rush, Alexander M.; Bart van Merriënboer; Joulin, Armand; Mikolov, Tomas (2015). "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks". arXiv:1502.05698 [cs.AI].
  222. Marcus, Mitchell P.; Ann Marcinkiewicz, Mary; Santorini, Beatrice (1993). "Building a large annotated corpus of English: The Penn Treebank". Computational Linguistics 19 (2): 313–330. 
  223. Collins, Michael (2003). "Head-driven statistical models for natural language parsing". Computational Linguistics 29 (4): 589–637. doi:10.1162/089120103322753356. 
  224. Guyon, Isabelle, et al., eds. Feature extraction: foundations and applications. Vol. 207. Springer, 2008.
  225. Lin, Yuri, et al. "Syntactic annotations for the google books ngram corpus." Proceedings of the ACL 2012 system demonstrations. Association for Computational Linguistics, 2012.
  226. Krishnamoorthy, Niveda et al. (2013). "Generating Natural-Language Video Descriptions Using Text-Mined Knowledge". AAAI 1. 
  227. Luyckx, Kim, and Walter Daelemans. "Personae: a Corpus for Author and Personality Prediction from Text." LREC. 2008.
  228. Solorio, Thamar, Ragib Hasan, and Mainul Mizan. "A case study of sockpuppet detection in wikipedia." Workshop on Language Analysis in Social Media (LASM) at NAACL HLT. 2013.
  229. Ciarelli, Patrick Marques, and Elias Oliveira. "Agglomeration and elimination of terms for dimensionality reduction." Intelligent Systems Design and Applications, 2009. ISDA'09. Ninth International Conference on. IEEE, 2009.
  230. Zhou, Mingyuan, Oscar Hernan Madrid Padilla, and James G. Scott. "Priors for random count matrices derived from a family of negative binomial processes." Journal of the American Statistical Association just-accepted (2015): 00–00.
  231. Kotzias, Dimitrios, et al. "From group to individual labels using deep features." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.
  232. Ning, Yue; Muthiah, Sathappan; Rangwala, Huzefa; Ramakrishnan, Naren (2016). "Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning". arXiv:1602.08033 [cs.SI].
  233. Buza, Krisztian. "Feedback prediction for blogs."Data analysis, machine learning and knowledge discovery. Springer International Publishing, 2014. 145–152.
  234. Soysal, Ömer M (2015). "Association rule mining with mostly associated sequential patterns". Expert Systems with Applications 42 (5): 2582–2592. doi:10.1016/j.eswa.2014.10.049. 
  235. Bowman, Samuel, et al. "A large annotated corpus for learning natural language inference." Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). ACL, 2015.
  236. "DSL Corpus Collection". 
  238. M. Versteegh, R. Thiollière, T. Schatz, X.-N. Cao, X. Anguera, A. Jansen, and E. Dupoux (2015). "The Zero Resource Speech Challenge 2015," in INTERSPEECH-2015.
  239. M. Versteegh, X. Anguera, A. Jansen, and E. Dupoux, (2016). "The Zero Resource Speech Challenge 2015: Proposed Approaches and Results," in SLTU-2016.
  240. Sakar, Betul Erdogdu et al. (2013). "Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings". Biomedical and Health Informatics, IEEE Journal of 17 (4): 828–834. doi:10.1109/jbhi.2013.2245674. PMID 25055311. 
  241. Zhao, Shunan, et al. "Automatic detection of expressed emotion in Parkinson's disease." Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014.
  242. Used in: Hammami, Nacereddine, and Mouldi Bedda. "Improved tree model for arabic speech recognition." Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. Vol. 5. IEEE, 2010.
  243. Maaten, Laurens. "Learning discriminative fisher kernels." Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011.
  244. Cole, Ronald, and Mark Fanty. "Spoken letter recognition." Proc. Third DARPA Speech and Natural Language Workshop. 1990.
  245. Chapelle, Olivier; Sindhwani, Vikas; Keerthi, Sathiya S. (2008). "Optimization techniques for semi-supervised support vector machines". The Journal of Machine Learning Research 9: 203–233. 
  246. Kudo, Mineichi; Toyama, Jun; Shimbo, Masaru (1999). "Multidimensional curve classification using passing-through regions". Pattern Recognition Letters 20 (11): 1103–1111. doi:10.1016/s0167-8655(99)00077-x. 
  247. Jaeger, Herbert et al. (2007). "Optimization and applications of echo state networks with leaky-integrator neurons". Neural Networks 20 (3): 335–352. doi:10.1016/j.neunet.2007.04.016. PMID 17517495. 
  248. Tsanas, Athanasios et al. (2010). "Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests" (Submitted manuscript). Biomedical Engineering, IEEE Transactions on 57 (4): 884–893. doi:10.1109/tbme.2009.2036000. PMID 19932995. 
  249. Clifford, Gari D.; Clifton, David (2012). "Wireless technology in disease management and medicine". Annual Review of Medicine 63: 479–492. doi:10.1146/annurev-med-051210-114650. PMID 22053737. 
  250. Zue, Victor; Seneff, Stephanie; Glass, James (1990). "Speech database development at MIT: TIMIT and beyond". Speech Communication 9 (4): 351–356. doi:10.1016/0167-6393(90)90010-7. 
  251. Kapadia, Sadik, Valtcho Valtchev, and S. J. Young. "MMI training for continuous phoneme recognition on the TIMIT database." Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on. Vol. 2. IEEE, 1993.
  252. Halabi, Nawar (2016). Modern Standard Arabic Phonetics for Speech Synthesis (PDF) (PhD Thesis). University of Southampton, School of Electronics and Computer Science.
  253. Saber MalekzadeH, Mohammad Hossein Gholizadeh, Seyed Naser Razavi "Full Persian Vowel recognition with MFCC and ANN on PCVC speech dataset" (PDF). 5th International conference of electrical engineering, computer science and information technology, Iran, Tehran, 2018.
  254. Zhou, Fang, Q. Claire, and Ross D. King. "Predicting the geographical origin of music." Data Mining (ICDM), 2014 IEEE International Conference on. IEEE, 2014.
  255. Saccenti, Edoardo; Camacho, José (2015). "On the use of the observation‐wise k‐fold operation in PCA cross‐validation". Journal of Chemometrics 29 (8): 467–478. doi:10.1002/cem.2726. 
  256. Bertin-Mahieux, Thierry, et al. "The million song dataset." ISMIR 2011: Proceedings of the 12th International Society for Music Information Retrieval Conference, 24–28 October 2011, Miami, Florida. University of Miami, 2011.
  257. Henaff, Mikael et al. (2011). "Unsupervised learning of sparse features for scalable audio classification". ISMIR 11. 
  258. Defferrard, Michaël; Benzi, Kirell; Vandergheynst, Pierre; Bresson, Xavier (6 December 2016). "FMA: A Dataset For Music Analysis". arXiv:1612.01840 [cs.SD].
  259. Esposito, Roberto; Radicioni, Daniele P. (2009). "Carpediem: Optimizing the viterbi algorithm and applications to supervised sequential learning". The Journal of Machine Learning Research 10: 1851–1880. 
  260. Sourati, Jamshid et al. (2016). "Classification Active Learning Based on Mutual Information". Entropy 18 (2): 51. doi:10.3390/e18020051. Bibcode2016Entrp..18...51S. 
  261. Salamon, Justin; Jacoby, Christopher; Bello, Juan Pablo. "A dataset and taxonomy for urban sound research." Proceedings of the ACM International Conference on Multimedia. ACM, 2014.
  262. Lagrange, Mathieu; Lafay, Grégoire; Rossignol, Mathias; Benetos, Emmanouil; Roebel, Axel (2015). "An evaluation framework for event detection using a morphological model of acoustic scenes". arXiv:1502.00141 [stat.ML].
  263. Gemmeke, Jort F., et al. "Audio Set: An ontology and human-labeled dataset for audio events." IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2017.
  264. "Watch out, birders: Artificial intelligence has learned to spot birds from their songs" (in en). Science | AAAS. 18 July 2018. Retrieved 22 July 2018. 
  265. "Bird Audio Detection challenge". 3 May 2016. Retrieved 22 July 2018. 
  266. The CAIDA UCSD Dataset on the Witty Worm – 19–24 March 2004,
  267. Chen, Zesheng, and Chuanyi Ji. "Optimal worm-scanning method using vulnerable-host distributions." International Journal of Security and Networks 2.1–2 (2007): 71–80.
  268. Kachuee, Mohamad, et al. "Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time." Circuits and Systems (ISCAS), 2015 IEEE International Symposium on. IEEE, 2015.
  269. PhysioBank, PhysioToolkit. "PhysioNet: components of a new research resource for complex physiologic signals." Circulation. v101 i23. e215-e220.
  270. Vergara, Alexander et al. (2012). "Chemical gas sensor drift compensation using classifier ensembles". Sensors and Actuators B: Chemical 166: 320–329. doi:10.1016/j.snb.2012.01.074. 
  271. Korotcenkov, G.; Cho, B. K. (2014). "Engineering approaches to improvement of conductometric gas sensor parameters. Part 2: Decrease of dissipated (consumable) power and improvement stability and reliability". Sensors and Actuators B: Chemical 198: 316–341. doi:10.1016/j.snb.2014.03.069. 
  272. Quinlan, John R (1992). "Learning with continuous classes". 5th Australian Joint Conference on Artificial Intelligence 92. 
  273. Merz, Christopher J.; Pazzani, Michael J. (1999). "A principal components approach to combining regression estimates". Machine Learning 36 (1–2): 9–32. doi:10.1023/a:1007507221352. 
  274. Torres-Sospedra, Joaquin, et al. "UJIIndoorLoc-Mag: A new database for magnetic field-based localization problems." Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on. IEEE, 2015.
  275. Berkvens, Rafael, Maarten Weyn, and Herbert Peremans. "Mean Mutual Information of Probabilistic Wi-Fi Localization." Indoor Positioning and Indoor Navigation (IPIN), 2015 International Conference on. Banff, Canada: IPIN. 2015.
  276. Paschke, Fabian, et al. "Sensorlose Zustandsüberwachung an Synchronmotoren."Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5.-6. Dezember 2013. KIT Scientific Publishing, 2013.
  277. Lessmeier, Christian, et al. "Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems."
  278. Ugulino, Wallace, et al. "Wearable computing: Accelerometers’ data classification of body postures and movements." Advances in Artificial Intelligence-SBIA 2012. Springer Berlin Heidelberg, 2012. 52–61.
  279. Schneider, Jan et al. (2015). "Augmenting the senses: a review on sensor-based learning support". Sensors 15 (2): 4097–4133. doi:10.3390/s150204097. PMID 25679313. 
  280. Madeo, Renata CB, Clodoaldo AM Lima, and Sarajane M. Peres. "Gesture unit segmentation using support vector machines: segmenting gestures from rest positions." Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013.
  281. Lun, Roanna; Zhao, Wenbing (2015). "A survey of applications and human motion recognition with Microsoft Kinect". International Journal of Pattern Recognition and Artificial Intelligence 29 (5): 1555008. doi:10.1142/s0218001415550083. 
  282. Theodoridis, Theodoros, and Huosheng Hu. "Action classification of 3d human models using dynamic ANNs for mobile robot surveillance."Robotics and Biomimetics, 2007. ROBIO 2007. IEEE International Conference on. IEEE, 2007.
  283. Etemad, Seyed Ali, and Ali Arya. "3D human action recognition and style transformation using resilient backpropagation neural networks." Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on. Vol. 4. IEEE, 2009.
  284. Anguita, Davide, et al. "Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine." Ambient assisted living and home care. Springer Berlin Heidelberg, 2012. 216–223.
  285. Su, Xing; Tong, Hanghang; Ji, Ping (2014). "Activity recognition with smartphone sensors". Tsinghua Science and Technology 19 (3): 235–249. doi:10.1109/tst.2014.6838194. 
  286. Kadous, Mohammed Waleed. Temporal classification: Extending the classification paradigm to multivariate time series. Diss. The University of New South Wales, 2002.
  287. Graves, Alex, et al. "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks." Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
  288. Velloso, Eduardo, et al. "Qualitative activity recognition of weight lifting exercises."Proceedings of the 4th Augmented Human International Conference. ACM, 2013.
  289. Mortazavi, Bobak Jack, et al. "Determining the single best axis for exercise repetition recognition and counting on smartwatches." Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on. IEEE, 2014.
  290. Sapsanis, Christos, et al. "Improving EMG based Classification of basic hand movements using EMD." Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013.
  291. 291.0 291.1 Andrianesis, Konstantinos; Tzes, Anthony (2015). "Development and control of a multifunctional prosthetic hand with shape memory alloy actuators". Journal of Intelligent & Robotic Systems 78 (2): 257–289. doi:10.1007/s10846-014-0061-6. 
  292. Banos, Oresti et al. (2014). "Dealing with the effects of sensor displacement in wearable activity recognition". Sensors 14 (6): 9995–10023. doi:10.3390/s140609995. PMID 24915181. 
  293. Stisen, Allan, et al. "Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition."Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 2015.
  294. Bhattacharya, Sourav, and Nicholas D. Lane. "From Smart to Deep: Robust Activity Recognition on Smartwatches using Deep Learning."
  295. Bacciu, Davide et al. (2014). "An experimental characterization of reservoir computing in ambient assisted living applications". Neural Computing and Applications 24 (6): 1451–1464. doi:10.1007/s00521-013-1364-4. 
  296. Palumbo, Filippo, et al. "Multisensor data fusion for activity recognition based on reservoir computing." Evaluating AAL systems through competitive benchmarking. Springer Berlin Heidelberg, 2013. 24-35.
  297. Reiss, Attila, and Didier Stricker. "Introducing a new benchmarked dataset for activity monitoring."Wearable Computers (ISWC), 2012 16th International Symposium on. IEEE, 2012.
  298. Roggen, Daniel, et al. "OPPORTUNITY: Towards opportunistic activity and context recognition systems." World of Wireless, Mobile and Multimedia Networks & Workshops, 2009. WoWMoM 2009. IEEE International Symposium on a. IEEE, 2009.
  299. Kurz, Marc, et al. "Dynamic quantification of activity recognition capabilities in opportunistic systems." Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 2011.
  300. Sztyler, Timo, and Heiner Stuckenschmidt. "On-body localization of wearable devices: an investigation of position-aware activity recognition." Pervasive Computing and Communications (PerCom), 2016 IEEE International Conference on. IEEE, 2016.
  301. Zhi, Ying Xuan; Lukasik, Michelle; Li, Michael H.; Dolatabadi, Elham; Wang, Rosalie H.; Taati, Babak (2018). "Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy". IEEE Journal of Translational Engineering in Health and Medicine 6: 1–7. doi:10.1109/JTEHM.2017.2780836. ISSN 2168-2372. PMID 29404226. 
  302. Dolatabadi, Elham; Zhi, Ying Xuan; Ye, Bing; Coahran, Marge; Lupinacci, Giorgia; Mihailidis, Alex; Wang, Rosalie; Taati, Babak (2017-05-23). The toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy. ACM. pp. 375–381. doi:10.1145/3154862.3154925. ISBN 9781450363631. 
  303. "Toronto Rehab Stroke Pose Dataset". 
  304. Aeberhard, S., D. Coomans, and O. De Vel. "Comparison of classifiers in high dimensional settings." Dept. Math. Statist., James Cook Univ., North Queensland, Australia, Tech. Rep 92-02 (1992).
  305. Basu, Sugato. "Semi-supervised clustering with limited background knowledge." AAAI. 2004.
  306. Tüfekci, Pınar (2014). "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods". International Journal of Electrical Power & Energy Systems 60: 126–140. doi:10.1016/j.ijepes.2014.02.027. 
  307. Kaya, Heysem, Pınar Tüfekci, and Fikret S. Gürgen. "Local and global learning methods for predicting power of a combined gas & steam turbine." International conference on emerging trends in computer and electronics engineering (ICETCEE'2012), Dubai. 2012.
  308. Baldi, Pierre; Sadowski, Peter; Whiteson, Daniel (2014). "Searching for exotic particles in high-energy physics with deep learning". Nature Communications 5: 2014. doi:10.1038/ncomms5308. PMID 24986233. Bibcode2014NatCo...5E4308B. 
  309. 309.0 309.1 Baldi, Pierre; Sadowski, Peter; Whiteson, Daniel (2015). "Enhanced Higgs Boson to τ+ τ− Search with Deep Learning". Physical Review Letters 114 (11): 111801. doi:10.1103/physrevlett.114.111801. PMID 25839260. Bibcode2015PhRvL.114k1801B. 
  310. 310.0 310.1 "The Higgs Machine Learning Challenge". 
  311. Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, and Daniel Whiteson. 'Parameterized Machine Learning for High-Energy Physics.' In submission.
  312. Ortigosa, I.; Lopez, R.; Garcia, J.. "A neural networks approach to residuary resistance of sailing yachts prediction". Proceedings of the International Conference on Marine Engineering MARINE 2007. 
  313. Gerritsma, J., R. Onnink, and A. Versluis.Geometry, resistance and stability of the delft systematic yacht hull series. Delft University of Technology, 1981.
  314. Liu, Huan, and Hiroshi Motoda. Feature extraction, construction and selection: A data mining perspective. Springer Science & Business Media, 1998.
  315. Reich, Yoram. Converging to Ideal Design Knowledge by Learning. [Carnegie Mellon University], Engineering Design Research Center, 1989.
  316. Todorovski, Ljupčo, and Sašo Džeroski.Experiments in meta-level learning with ILP. Springer Berlin Heidelberg, 1999.
  317. Wang, Yong. A new approach to fitting linear models in high dimensional spaces. Diss. The University of Waikato, 2000.
  318. Kibler, Dennis; Aha, David W.; Albert, Marc K. (1989). "Instance‐based prediction of real‐valued attributes". Computational Intelligence 5 (2): 51–57. doi:10.1111/j.1467-8640.1989.tb00315.x. 
  319. Palmer, Christopher R., and Christos Faloutsos. "Electricity based external similarity of categorical attributes." Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2003. 486–500.
  320. Tsanas, Athanasios; Xifara, Angeliki (2012). "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools". Energy and Buildings 49: 560–567. doi:10.1016/j.enbuild.2012.03.003. 
  321. De Wilde, Pieter (2014). "The gap between predicted and measured energy performance of buildings: A framework for investigation". Automation in Construction 41: 40–49. doi:10.1016/j.autcon.2014.02.009. 
  322. Brooks, Thomas F., D. Stuart Pope, and Michael A. Marcolini. Airfoil self-noise and prediction. Vol. 1218. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1989.
  323. Draper, David. "Assessment and propagation of model uncertainty." Journal of the Royal Statistical Society, Series B (Methodological) (1995): 45–97.
  324. Lavine, Michael (1991). "Problems in extrapolation illustrated with space shuttle O-ring data". Journal of the American Statistical Association 86 (416): 919–921. doi:10.1080/01621459.1991.10475132. 
  325. Wang, Jun, Bei Yu, and Les Gasser. "Concept tree based clustering visualization with shaded similarity matrices." Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on. IEEE, 2002.
  326. Pettengill, Gordon H., et al. "Magellan: Radar performance and data products." Science252.5003 (1991): 260–265.
  327. 327.0 327.1 Aharonian, F. et al. (2008). "Energy spectrum of cosmic-ray electrons at TeV energies". Physical Review Letters 101 (26): 261104. doi:10.1103/PhysRevLett.101.261104. PMID 19437632. Bibcode2008PhRvL.101z1104A. 
  328. Bock, R. K. et al. (2004). "Methods for multidimensional event classification: a case study using images from a Cherenkov gamma-ray telescope". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 516 (2): 511–528. doi:10.1016/j.nima.2003.08.157. Bibcode2004NIMPA.516..511B. 
  329. Li, Jinyan et al. (2004). "Deeps: A new instance-based lazy discovery and classification system". Machine Learning 54 (2): 99–124. doi:10.1023/b:mach.0000011804.08528.7d. 
  330. Siebert, Lee, and Tom Simkin. "Volcanoes of the world: an illustrated catalog of Holocene volcanoes and their eruptions." (2014).
  331. Sikora, Marek; Wróbel, Łukasz (2010). "Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines". Archives of Mining Sciences 55 (1): 91–114. 
  332. Sikora, Marek, and Beata Sikora. "Rough natural hazards monitoring." Rough Sets: Selected Methods and Applications in Management and Engineering. Springer London, 2012. 163–179.
  333. Yeh, I–C (1998). "Modeling of strength of high-performance concrete using artificial neural networks". Cement and Concrete Research 28 (12): 1797–1808. doi:10.1016/s0008-8846(98)00165-3. 
  334. Zarandi, MH Fazel et al. (2008). "Fuzzy polynomial neural networks for approximation of the compressive strength of concrete". Applied Soft Computing 8 (1): 488–498. doi:10.1016/j.asoc.2007.02.010. 
  335. Yeh, I. "Modeling slump of concrete with fly ash and superplasticizer." Computers and Concrete5.6 (2008): 559–572.
  336. Gencel, Osman et al. (2011). "Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete". Construction and Building Materials 25 (8): 3486–3494. doi:10.1016/j.conbuildmat.2011.03.040. 
  337. Dietterich, Thomas G., et al. "A comparison of dynamic reposing and tangent distance for drug activity prediction." Advances in Neural Information Processing Systems (1994): 216–216.
  338. Buscema, Massimo, William J. Tastle, and Stefano Terzi. "Meta net: A new meta-classifier family."Data Mining Applications Using Artificial Adaptive Systems. Springer New York, 2013. 141–182.
  339. Used; Ingber, Lester (1997). "Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography". Physical Review E 55 (4): 4578–4593. doi:10.1103/PhysRevE.55.4578. Bibcode1997PhRvE..55.4578I. 
  340. Ingber, Lester (1997). "Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography". Physical Review E 55 (4): 4578–4593. doi:10.1103/physreve.55.4578. Bibcode1997PhRvE..55.4578I. 
  341. Hoffmann, Ulrich et al. (2008). "An efficient P300-based brain–computer interface for disabled subjects". Journal of Neuroscience Methods 167 (1): 115–125. doi:10.1016/j.jneumeth.2007.03.005. PMID 17445904. 
  342. Donchin, Emanuel, Kevin M. Spencer, and Ranjith Wijesinghe. "The mental prosthesis: assessing the speed of a P300-based brain-computer interface."Rehabilitation Engineering, IEEE Transactions on8.2 (2000): 174-179.
  343. Detrano, Robert et al. (1989). "International application of a new probability algorithm for the diagnosis of coronary artery disease". The American Journal of Cardiology 64 (5): 304–310. doi:10.1016/0002-9149(89)90524-9. 
  344. Bradley, Andrew P (1997). "The use of the area under the ROC curve in the evaluation of machine learning algorithms". Pattern Recognition 30 (7): 1145–1159. doi:10.1016/s0031-3203(96)00142-2. 
  345. Street, W. Nick, William H. Wolberg, and Olvi L. Mangasarian. "Nuclear feature extraction for breast tumor diagnosis." IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology. International Society for Optics and Photonics, 1993.
  346. Demir, Cigdem, and Bülent Yener. "Automated cancer diagnosis based on histopathological images: a systematic survey." Rensselaer Polytechnic Institute, Tech. Rep (2005).
  347. Abuse, Substance. "Mental Health Services Administration, Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-41, HHS Publication No.(SMA) 11-4658." Rockville, MD: Substance Abuse and Mental Health Services Administration 201 (2011).
  348. Hong, Zi-Quan; Yang, Jing-Yu (1991). "Optimal discriminant plane for a small number of samples and design method of classifier on the plane". Pattern Recognition 24 (4): 317–324. doi:10.1016/0031-3203(91)90074-f. 
  349. 349.0 349.1 Li, Jinyan, and Limsoon Wong. "Using rules to analyse bio-medical data: a comparison between C4. 5 and PCL." Advances in Web-Age Information Management. Springer Berlin Heidelberg, 2003. 254-265.
  350. Güvenir, H. Altay, et al. "A supervised machine learning algorithm for arrhythmia analysis."Computers in Cardiology 1997. IEEE, 1997.
  351. Lagus, Krista, et al. "Independent variable group analysis in learning compact representations for data." Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR'05), T. Honkela, V. Könönen, M. Pöllä, and O. Simula, Eds., Espoo, Finland. 2005.
  352. Strack, Beata, et al. "Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records." BioMed Research International 2014; 2014
  353. Rubin, Daniel J (2015). "Hospital readmission of patients with diabetes". Current Diabetes Reports 15 (4): 1–9. doi:10.1007/s11892-015-0584-7. PMID 25712258. 
  354. Antal, Bálint; Hajdu, András (2014). "An ensemble-based system for automatic screening of diabetic retinopathy". Knowledge-Based Systems 60 (2014): 20–27. doi:10.1016/j.knosys.2013.12.023. 
  355. Haloi, Mrinal (2015). "Improved Microaneurysm Detection using Deep Neural Networks". arXiv:1505.04424 [cs.CV].
  356. ELIE, Guillaume PATRY, Gervais GAUTHIER, Bruno LAY, Julien ROGER, Damien. "ADCIS Download Third Party: Messidor Database" (in en). 
  357. Decencière, Etienne; Zhang, Xiwei; Cazuguel, Guy; Lay, Bruno; Cochener, Béatrice; Trone, Caroline; Gain, Philippe; Ordonez, Richard et al. (2014-08-26). "FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE" (in en). Image Analysis & Stereology 33 (3): 231–234. doi:10.5566/ias.1155. ISSN 1854-5165. 
  358. Bagirov, A. M. et al. (2003). "Unsupervised and supervised data classification via nonsmooth and global optimization". Top 11 (1): 1–75. doi:10.1007/bf02578945. 
  359. Fung, Glenn, et al. "A fast iterative algorithm for fisher discriminant using heterogeneous kernels."Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
  360. Quinlan, John Ross, et al. "Inductive knowledge acquisition: a case study." Proceedings of the Second Australian Conference on Applications of expert systems. Addison-Wesley Longman Publishing Co., Inc., 1987.
  361. 361.0 361.1 Zhou, Zhi-Hua; Jiang, Yuan (2004). "NeC4. 5: neural ensemble based C4. 5". Knowledge and Data Engineering, IEEE Transactions on 16 (6): 770–773. doi:10.1109/tkde.2004.11. 
  362. Er, Orhan et al. (2012). "An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease". Computers & Electrical Engineering 38 (1): 75–81. doi:10.1016/j.compeleceng.2011.09.001. 
  363. Er, Orhan, A. Çetin Tanrikulu, and Abdurrahman Abakay. "Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma."Dicle Tıp Dergisi 42.1 (2015).
  364. Li, Michael H.; Mestre, Tiago A.; Fox, Susan H.; Taati, Babak (2017-07-25). "Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation". arXiv:1707.09416 [cs.CV].
  365. Li, Michael H.; Mestre, Tiago A.; Fox, Susan H.; Taati, Babak (May 2018). "Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features". Parkinsonism & Related Disorders. doi:10.1016/j.parkreldis.2018.04.036. ISSN 1353-8020. PMID 29748112. 
  366. "Parkinson's Vision-Based Pose Estimation Dataset | Kaggle". 
  367. Shannon, Paul et al. (2003). "Cytoscape: a software environment for integrated models of biomolecular interaction networks". Genome Research 13 (11): 2498–2504. doi:10.1101/gr.1239303. PMID 14597658. 
  368. Clark, David, Zoltan Schreter, and Anthony Adams. "A quantitative comparison of dystal and backpropagation." Proceedings of 1996 Australian Conference on Neural Networks. 1996.
  369. Jiang, Yuan, and Zhi-Hua Zhou. "Editing training data for kNN classifiers with neural network ensemble." Advances in Neural Networks–ISNN 2004. Springer Berlin Heidelberg, 2004. 356–361.
  370. Ontañón, Santiago, and Enric Plaza. "On similarity measures based on a refinement lattice." Case-Based Reasoning Research and Development. Springer Berlin Heidelberg, 2009. 240–255.
  371. Higuera, Clara; Gardiner, Katheleen J.; Cios, Krzysztof J. (2015). "Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome". PLOS ONE 10 (6): e0129126. doi:10.1371/journal.pone.0129126. PMID 26111164. Bibcode2015PLoSO..1029126H. 
  372. Ahmed, Md Mahiuddin et al. (2015). "Protein dynamics associated with failed and rescued learning in the Ts65Dn mouse model of Down syndrome". PLOS ONE 10 (3): e0119491. doi:10.1371/journal.pone.0119491. PMID 25793384. Bibcode2015PLoSO..1019491A. 
  373. Cortez, Paulo, and Aníbal de Jesus Raimundo Morais. "A data mining approach to predict forest fires using meteorological data." (2007).
  374. Farquad, M. A. H.; Ravi, V.; Raju, S. Bapi (2010). "Support vector regression based hybrid rule extraction methods for forecasting". Expert Systems with Applications 37 (8): 5577–5589. doi:10.1016/j.eswa.2010.02.055. 
  375. Fisher, Ronald A (1936). "The use of multiple measurements in taxonomic problems". Annals of Eugenics 7 (2): 179–188. doi:10.1111/j.1469-1809.1936.tb02137.x. 
  376. Ghahramani, Zoubin, and Michael I. Jordan. "Supervised learning from incomplete data via an EM approach." Advances in neural information processing systems 6. 1994.
  377. Mallah, Charles; Cope, James; Orwell, James (2013). "Plant leaf classification using probabilistic integration of shape, texture and margin features". Signal Processing, Pattern Recognition and Applications 5: 1. 
  378. Yahiaoui, Itheri, Olfa Mzoughi, and Nozha Boujemaa. "Leaf shape descriptor for tree species identification." Multimedia and Expo (ICME), 2012 IEEE International Conference on. IEEE, 2012.
  379. Langley, PAT (2014). "Trading off simplicity and coverage in incremental concept learning". Machine Learning Proceedings 1988: 73. 
  380. Tan, Ming, and Larry Eshelman. "Using weighted networks to represent classification knowledge in noisy domains." Proceedings of the Fifth International Conference on Machine Learning. 2014.
  381. Charytanowicz, Małgorzata, et al. "Complete gradient clustering algorithm for features analysis of x-ray images." Information technologies in biomedicine. Springer Berlin Heidelberg, 2010. 15–24.
  382. Sanchez, Mauricio A. et al. (2014). "Fuzzy granular gravitational clustering algorithm for multivariate data". Information Sciences 279: 498–511. doi:10.1016/j.ins.2014.04.005. 
  383. Blackard, Jock A.; Dean, Denis J. (1999). "Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables". Computers and Electronics in Agriculture 24 (3): 131–151. doi:10.1016/s0168-1699(99)00046-0. 
  384. Fürnkranz, Johannes. "Round robin rule learning."Proceedings of the 18th International Conference on Machine Learning (ICML-01): 146--153. 2001.
  385. Li, Song; Assmann, Sarah M.; Albert, Réka (2006). "Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling". PLoS Biol 4 (10): e312. doi:10.1371/journal.pbio.0040312. PMID 16968132. 
  386. Munisami, Trishen et al. (2015). "Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers". Procedia Computer Science 58: 740–747. doi:10.1016/j.procs.2015.08.095. 
  387. Li, Bai (2016). "Atomic potential matching: An evolutionary target recognition approach based on edge features". Optik-International Journal for Light and Electron Optics 127 (5): 3162–3168. doi:10.1016/j.ijleo.2015.11.186. Bibcode2016Optik.127.3162L. 
  388. Nilsback, Maria-Elena, and Andrew Zisserman. "A visual vocabulary for flower classification."Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.
  389. Giselsson, Thomas M.; et al. (2017). "A Public Image Database for Benchmark of Plant Seedling Classification Algorithms". arXiv:1711.05458 [cs.CV]. Explicit use of et al. in: |last2= (help)
  390. Muresan, Horea; Oltean, Mihai (2018). "Fruit recognition from images using deep learning". Acta Univ. Sapientiae, Informatica 10 (1): 26–42. doi:10.13140/RG.2.2.22059.95527. 
  391. Muresan, Horea; Oltean, Mihai (2017). "A dataset with fruit images on Kaggle". 
  392. Nakai, Kenta; Kanehisa, Minoru (1991). "Expert system for predicting protein localization sites in gram‐negative bacteria". Proteins: Structure, Function, and Bioinformatics 11 (2): 95–110. doi:10.1002/prot.340110203. PMID 1946347. 
  393. Ling, Charles X., et al. "Decision trees with minimal costs." Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.
  394. Mahé, Pierre, et al. "Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum." Bioinformatics (2014): btu022.
  395. Barbano, Duane et al. (2015). "Rapid characterization of microalgae and microalgae mixtures using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)". PLOS ONE 10 (8): e0135337. doi:10.1371/journal.pone.0135337. PMID 26271045. Bibcode2015PLoSO..1035337B. 
  396. Horton, Paul; Nakai, Kenta. "A probabilistic classification system for predicting the cellular localization sites of proteins". Ismb 4: 1996. 
  397. Allwein, Erin L.; Schapire, Robert E.; Singer, Yoram (2001). "Reducing multiclass to binary: A unifying approach for margin classifiers". The Journal of Machine Learning Research 1: 113–141. 
  398. Mayr, Andreas; Klambauer, Guenter; Unterthiner, Thomas; Hochreiter, Sepp (2016). "DeepTox: Toxicity Prediction Using Deep Learning". Frontiers in Environmental Science 3: 80. doi:10.3389/fenvs.2015.00080. 
  399. Lavin, Alexander; Ahmad, Subutai (12 October 2015). "Evaluating Real-Time Anomaly Detection Algorithms -- the Numenta Anomaly Benchmark". Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark. pp. 38. doi:10.1109/ICMLA.2015.141. ISBN 978-1-5090-0287-0. 
  400. Campos, Guilherme O.; Zimek, Arthur; Sander, Jörg; Campello, Ricardo J. G. B.; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E. (2016). "On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study". Data Mining and Knowledge Discovery 30 (4): 891. doi:10.1007/s10618-015-0444-8. ISSN 1384-5810. 
  401. Ann-Kathrin Hartmann, Tommaso Soru, Edgard Marx. Generating a Large Dataset for Neural Question Answering over the DBpedia Knowledge Base. 2018.
  402. Tommaso Soru, Edgard Marx. Diego Moussallem, Andre Valdestilhas, Diego Esteves, Ciro Baron. SPARQL as a Foreign Language. 2018.
  403. Brown, Michael Scott, Michael J. Pelosi, and Henry Dirska. "Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks." Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2013. 27–41.
  404. Shen, Kao-Yi; Tzeng, Gwo-Hshiung (2015). "Fuzzy Inference-Enhanced VC-DRSA Model for Technical Analysis: Investment Decision Aid". International Journal of Fuzzy Systems 17 (3): 375–389. doi:10.1007/s40815-015-0058-8. 
  405. Quinlan, J. Ross (1987). "Simplifying decision trees". International Journal of Man-machine Studies 27 (3): 221–234. doi:10.1016/s0020-7373(87)80053-6. 
  406. Hamers, Bart; Suykens, Johan AK; De Moor, Bart (2003). "Coupled transductive ensemble learning of kernel models". Journal of Machine Learning Research 1: 1–48. 
  407. Shmueli, Galit, Ralph P. Russo, and Wolfgang Jank. "The BARISTA: a model for bid arrivals in online auctions." The Annals of Applied Statistics(2007): 412–441.
  408. Peng, Jie, and Hans-Georg Müller. "Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions." The Annals of Applied Statistics (2008): 1056–1077.
  409. Eggermont, Jeroen, Joost N. Kok, and Walter A. Kosters. "Genetic programming for data classification: Partitioning the search space."Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004.
  410. Moro, Sérgio; Cortez, Paulo; Rita, Paulo (2014). "A data-driven approach to predict the success of bank telemarketing". Decision Support Systems 62: 22–31. doi:10.1016/j.dss.2014.03.001. 
  411. Payne, Richard D.; Mallick, Bani K. (2014). "Bayesian Big Data Classification: A Review with Complements". arXiv:1411.5653 [stat.ME].
  412. Akbilgic, Oguz; Bozdogan, Hamparsum; Balaban, M. Erdal (2014). "A novel Hybrid RBF Neural Networks model as a forecaster". Statistics and Computing 24 (3): 365–375. doi:10.1007/s11222-013-9375-7. 
  413. Jabin, Suraiya. "Stock market prediction using feed-forward artificial neural network." Int. J. Comput. Appl. (IJCA) 99.9 (2014).
  414. Yeh, I-Cheng; Che-hui, Lien (2009). "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients". Expert Systems with Applications 36 (2): 2473–2480. doi:10.1016/j.eswa.2007.12.020. 
  415. Lin, Shu Ling (2009). "A new two-stage hybrid approach of credit risk in banking industry". Expert Systems with Applications 36 (4): 8333–8341. doi:10.1016/j.eswa.2008.10.015. 
  416. Pelckmans, Kristiaan et al. (2005). "The differogram: Non-parametric noise variance estimation and its use for model selection". Neurocomputing 69 (1): 100–122. doi:10.1016/j.neucom.2005.02.015. 
  417. Bay, Stephen D. et al. (2000). "The UCI KDD archive of large data sets for data mining research and experimentation". ACM SIGKDD Explorations Newsletter 2 (2): 81–85. doi:10.1145/380995.381030. 
  418. Lucas, D. D. et al. (2015). "Designing optimal greenhouse gas observing networks that consider performance and cost". Geoscientific Instrumentation, Methods and Data Systems 4 (1): 121. doi:10.5194/gi-4-121-2015. Bibcode2015GI......4..121L. 
  419. Pales, Jack C.; Keeling, Charles D. (1965). "The concentration of atmospheric carbon dioxide in Hawaii". Journal of Geophysical Research 70 (24): 6053–6076. doi:10.1029/jz070i024p06053. Bibcode1965JGR....70.6053P. 
  420. Sigillito, Vincent G., et al. "Classification of radar returns from the ionosphere using neural networks." Johns Hopkins APL Technical Digest10.3 (1989): 262–266.
  421. Zhang, Kun, and Wei Fan. "Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond." Knowledge and Information Systems14.3 (2008): 299–326.
  422. Reich, Brian J., Montserrat Fuentes, and David B. Dunson. "Bayesian spatial quantile regression." Journal of the American Statistical Association (2012).
  423. Kohavi, Ron (1996). "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". KDD 96. 
  424. Oza, Nikunj C., and Stuart Russell. "Experimental comparisons of online and batch versions of bagging and boosting." Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001.
  425. Bay, Stephen D (2001). "Multivariate discretization for set mining". Knowledge and Information Systems 3 (4): 491–512. doi:10.1007/pl00011680. 
  426. Ruggles, Steven (1995). "Sample designs and sampling errors". Historical Methods: A Journal of Quantitative and Interdisciplinary History 28 (1): 40–46. doi:10.1080/01615440.1995.9955312. 
  427. Meek, Christopher, Bo Thiesson, and David Heckerman. "The Learning Curve Method Applied to Clustering." AISTATS. 2001.
  428. Fanaee-T, Hadi; Gama, Joao (2013). "Event labeling combining ensemble detectors and background knowledge". Progress in Artificial Intelligence 2 (2–3): 113–127. doi:10.1007/s13748-013-0040-3. 
  429. Giot, Romain, and Raphaël Cherrier. "Predicting bikeshare system usage up to one day ahead." Computational intelligence in vehicles and transportation systems (CIVTS), 2014 IEEE symposium on. IEEE, 2014.
  430. Moreira-Matias, Luis et al. (2013). "Predicting taxi–passenger demand using streaming data". Intelligent Transportation Systems, IEEE Transactions on 14 (3): 1393–1402. doi:10.1109/tits.2013.2262376. 
  431. Hwang, Ren-Hung; Hsueh, Yu-Ling; Chen, Yu-Ting (2015). "An effective taxi recommender system based on a spatio-temporal factor analysis model". Information Sciences 314: 28–40. doi:10.1016/j.ins.2015.03.068. 
  432. Meusel, Robert, et al. "The Graph Structure in the Web—Analyzed on Different Aggregation Levels."The Journal of Web Science 1.1 (2015).
  433. Kushmerick, Nicholas. "Learning to remove internet advertisements." Proceedings of the third annual conference on Autonomous Agents. ACM, 1999.
  434. Fradkin, Dmitriy, and David Madigan. "Experiments with random projections for machine learning."Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003.
  435. This data was used in the American Statistical Association Statistical Graphics and Computing Sections 1999 Data Exposition.
  436. Ma, Justin, et al. "Identifying suspicious URLs: an application of large-scale online learning."Proceedings of the 26th annual international conference on machine learning. ACM, 2009.
  437. Levchenko, Kirill, et al. "Click trajectories: End-to-end analysis of the spam value chain." Security and Privacy (SP), 2011 IEEE Symposium on. IEEE, 2011.
  438. Mohammad, Rami M., Fadi Thabtah, and Lee McCluskey. "An assessment of features related to phishing websites using an automated technique."Internet Technology And Secured Transactions, 2012 International Conference for. IEEE, 2012.
  439. Singh, Ashishkumar, et al. "Clustering Experiments on Big Transaction Data for Market Segmentation." Proceedings of the 2014 International Conference on Big Data Science and Computing. ACM, 2014.
  440. Bollacker, Kurt, et al. "Freebase: a collaboratively created graph database for structuring human knowledge." Proceedings of the 2008 ACM SIGMOD international conference on Management of data. ACM, 2008.
  441. Mintz, Mike, et al. "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 2009.
  442. Mesterharm, Chris, and Michael J. Pazzani. "Active learning using on-line algorithms."Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011.
  443. Wang, Shusen; Zhang, Zhihua (2013). "Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling". The Journal of Machine Learning Research 14 (1): 2729–2769. 
  444. Cattral, Robert, Franz Oppacher, and Dwight Deugo. "Evolutionary data mining with automatic rule generalization." Recent Advances in Computers, Computing and Communications(2002): 296–300.
  445. Burton, Ariel N., and Paul HJ Kelly. "Performance prediction of paging workloads using lightweight tracing." Future Generation Computer Systems22.7 (2006): 784–793.
  446. Bain, Michael, and Stephen Muggleton. "Learning optimal chess strategies." Machine intelligence 13. Oxford University Press, Inc., 1994.
  447. Quilan, J. R. (1983). "Learning efficient classification procedures and their application to chess end games". Machine Learning: An Artificial Intelligence Approach 1. 
  448. Shapiro, Alen D. Structured induction in expert systems. Addison-Wesley Longman Publishing Co., Inc., 1987.
  449. Matheus, Christopher J.; Rendell, Larry A. (1989). "Constructive Induction on Decision Trees". IJCAI 89. 
  450. Belsley, David A., Edwin Kuh, and Roy E. Welsch. Regression diagnostics: Identifying influential data and sources of collinearity. Vol. 571. John Wiley & Sons, 2005.
  451. Ruotsalo, Tuukka; Aroyo, Lora; Schreiber, Guus (2009). "Knowledge-based linguistic annotation of digital cultural heritage collections". IEEE Intelligent Systems 2: 64–75. 
  452. Li, Lihong, et al. "Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.
  453. Yeung, Kam Fung, and Yanyan Yang. "A proactive personalized mobile news recommendation system." Developments in E-systems Engineering (DESE), 2010. IEEE, 2010.
  454. Gass, Susan E.; Roberts, J. Murray (2006). "The occurrence of the cold-water coral Lophelia pertusa (Scleractinia) on oil and gas platforms in the North Sea: colony growth, recruitment and environmental controls on distribution". Marine Pollution Bulletin 52 (5): 549–559. doi:10.1016/j.marpolbul.2005.10.002. PMID 16300800. 
  455. Gionis, Aristides; Mannila, Heikki; Tsaparas, Panayiotis (2007). "Clustering aggregation". ACM Transactions on Knowledge Discovery from Data (TKDD) 1 (1): 4. doi:10.1145/1217299.1217303. 
  456. Obradovic, Zoran, and Slobodan Vucetic.Challenges in Scientific Data Mining: Heterogeneous, Biased, and Large Samples. Technical Report, Center for Information Science and Technology Temple University, 2004.
  457. Van Der Putten, Peter; van Someren, Maarten (2000). "CoIL challenge 2000: The insurance company case". Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 9: 1–43. 
  458. Mao, K. Z. (2002). "RBF neural network center selection based on Fisher ratio class separability measure". Neural Networks, IEEE Transactions on 13 (5): 1211–1217. doi:10.1109/tnn.2002.1031953. PMID 18244518. 
  459. Olave, Manuel; Rajkovic, Vladislav; Bohanec, Marko (1989). "An application for admission in public school systems". Expert Systems in Public Administration 1: 145–160. 
  460. Lizotte, Daniel J., Omid Madani, and Russell Greiner. "Budgeted learning of nailve-bayes classifiers." Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 2002.
  461. Lebowitz, Michael (1986). "Concept learning in a rich input domain: Generalization-based memory". Machine Learning: An Artificial Intelligence Approach 2: 193–214. 
  462. Yeh, I-Cheng; Yang, King-Jang; Ting, Tao-Ming (2009). "Knowledge discovery on RFM model using Bernoulli sequence". Expert Systems with Applications 36 (3): 5866–5871. doi:10.1016/j.eswa.2008.07.018. 
  463. Lee, Wen-Chen; Cheng, Bor-Wen (2011). "An intelligent system for improving performance of blood donation". Journal of Quality Vol 18 (2): 173. 
  464. Schmidtmann, Irene, et al. "Evaluation des Krebsregisters NRW Schwerpunkt Record Linkage." Abschlußbericht vom 11 (2009).
  465. Sariyar, Murat; Borg, Andreas; Pommerening, Klaus (2011). "Controlling false match rates in record linkage using extreme value theory". Journal of Biomedical Informatics 44 (4): 648–654. doi:10.1016/j.jbi.2011.02.008. PMID 21352952. 
  466. Candillier, Laurent, and Vincent Lemaire. "Design and Analysis of the Nomao challenge Active Learning in the Real-World." Proceedings of the ALRA: Active Learning in Real-world Applications, Workshop ECML-PKDD. 2012.
  467. Marquez, Ivan Garrido. "A Domain Adaptation Method for Text Classification based on Self-adjusted Training Approach." (2013).
  468. Nagesh, Harsha S., Sanjay Goil, and Alok N. Choudhary. "Adaptive Grids for Clustering Massive Data Sets." SDM. 2001.
  469. Kuzilek, Jakub, et al. "OU Analyse: analysing at-risk students at The Open University." Learning Analytics Review (2015): 1–16.
  470. Siemens, George, et al. Open Learning Analytics: an integrated & modularized platform. Diss. Open University Press, 2011.
  471. Barlacchi, Gianni; De Nadai, Marco; Larcher, Roberto; Casella, Antonio; Chitic, Cristiana; Torrisi, Giovanni; Antonelli, Fabrizio; Vespignani, Alessandro et al. (2015). "A multi-source dataset of urban life in the city of Milan and the Province of Trentino". Scientific Data 2: 150055. doi:10.1038/sdata.2015.55. ISSN 2052-4463. PMID 26528394. Bibcode2015NatSD...250055B. 
  472. "OpenML: networked science in machine learning". SIGKDD Explorations 15 (2): 49–60. 2013. doi:10.1145/2641190.2641198. 
  473. "PMLB: a large benchmark suite for machine learning evaluation and comparison". BioData Mining 10: 36. 2017. doi:10.1186/s13040-017-0154-4. PMID 29238404.