Neural Networks for Pattern Recognition

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

A Tutorial on Support Vector Machines for Pattern Recognition

Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works

A tutorial on support vector regression

Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works

Bidirectional recurrent neural networks

Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works

The graph neural network model

Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works

Least Squares Support Vector Machine Classifiers

Suykens, J., & Vandewalle, J. (1999) · Neural Processing Letters · doi:10.1023/a:1018628609742 · Cited by 9,481 · see citing works

Conflict monitoring and cognitive control

Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.624 · Cited by 7,351 · see citing works

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm

Zhang, Y., Brady, M., & Smith, S. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906424 · Cited by 7,628 · see citing works

Statistical pattern recognition: a review

Jain, A. K., Duin, P., & Mao, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824819 · Cited by 6,795 · see citing works

Survey of Clustering Algorithms

Xu, R., & Wunsch, D. C. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.845141 · Cited by 6,223 · see citing works

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works

Semi-Supervised Learning

Chapelle, O., Schölkopf, B., & Zien, A. (2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,560 · see citing works

Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

Ho, D. E., Imai, K., King, G., & Stuart, E. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,411 · see citing works

Support Vector Data Description

Tax, D. M. J., & Duin, R. P. W. (2003) · Machine Learning · doi:10.1023/b:mach.0000008084.60811.49 · Cited by 3,553 · see citing works

An introduction to kernel-based learning algorithms

Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works

Detecting faces in images: a survey

Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,437 · see citing works

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · see citing works

Best practices for convolutional neural networks applied to visual document analysis

Simard, P., Steinkraus, D., & Platt, J. C. (2005) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2003.1227801 · Cited by 2,805 · see citing works

Point Set Registration: Coherent Point Drift

Myronenko, A., & Song, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.46 · Cited by 2,774 · see citing works

Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging

Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707 · see citing works

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644 · see citing works

The RIN: an RNA integrity number for assigning integrity values to RNA measurements

Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006) · BMC Molecular Biology · doi:10.1186/1471-2199-7-3 · Cited by 2,623 · see citing works

Empirical Asset Pricing via Machine Learning

Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545 · see citing works

Online and off-line handwriting recognition: a comprehensive survey

Plamondon, R., & Srihari, S. N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824821 · Cited by 2,508 · see citing works

Support vector machine classification and validation of cancer tissue samples using microarray expression data

Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.10.906 · Cited by 2,449 · see citing works

Knowledge-based analysis of microarray gene expression data by using support vector machines

Brown, M. P. S., Noble, W. S., Lin, D. M., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., & Haussler, D. H. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.1.262 · Cited by 2,310 · see citing works

Neural networks for short-term load forecasting: a review and evaluation

Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001) · IEEE Transactions on Power Systems · doi:10.1109/59.910780 · Cited by 2,229 · see citing works

KNN Model-Based Approach in Classification

Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39964-3_62 · Cited by 2,277 · see citing works

Face recognition based on fitting a 3D morphable model

Blanz, V., & Vetter, T. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1227983 · Cited by 2,023 · see citing works

Motor imagery and direct brain-computer communication

Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990 · see citing works

LSTM neural networks for language modeling

Sundermeyer, M., Schlüter, R., & Ney, H. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-65 · Cited by 1,963 · see citing works

Independent Component Analysis

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Understanding deep image representations by inverting them

Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works

Homo Heuristicus: Why Biased Minds Make Better Inferences

Gigerenzer, G., & Brighton, H. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2008.01006.x · Cited by 1,801 · see citing works

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Ahmed, M., Seraj, R., & Islam, S. M. S. (2020) · Electronics · doi:10.3390/electronics9081295 · Cited by 1,717 · see citing works

Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features

de Chazal, P., O'Dwyer, M., & Reilly, R. B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827359 · Cited by 1,685 · see citing works

Automatically characterizing large scale program behavior

Sherwood, T., Perelman, E., Hamerly, G., et al. (2002) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/605397.605403 · Cited by 1,682 · see citing works

Neural networks for classification: a survey

Zhang, G. (2000) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.897072 · Cited by 1,672 · see citing works

On the mathematical foundations of learning

Felipe Cucker, B., & Smale, S. (2001) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-01-00923-5 · Cited by 1,524 · see citing works

Machine Learning Force Fields

Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647 · see citing works

Dataset Shift in Machine Learning

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Approximation theory of the MLP model in neural networks

Pinkus, A. M. (1999) · Acta Numerica · doi:10.1017/s0962492900002919 · Cited by 1,542 · see citing works

11 TOPS photonic convolutional accelerator for optical neural networks

Xu, X., Tan, M., Corcoran, B., Wu, J., Boes, A., Nguyen, T. G., Chu, S. T., Little, B. E., Hicks, D. G., Morandotti, R., et al. (2021) · Nature · doi:10.1038/s41586-020-03063-0 · Cited by 1,531 · see citing works

Learning Low-Level Vision

Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026501619075 · Cited by 1,504 · see citing works

A survey on object detection in optical remote sensing images

Cheng, G., & Han, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.014 · Cited by 1,503 · see citing works

Artificial Neural Networks in Hydrology. I: Preliminary Concepts

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2000)5:2(115 · Cited by 1,477 · see citing works

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works

The importance of intrinsic disorder for protein phosphorylation

Iakoucheva, L. M. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh253 · Cited by 1,429 · see citing works

Deep learning in spiking neural networks

Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.12.002 · Cited by 1,420 · see citing works

Explaining nonlinear classification decisions with deep taylor decomposition

Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357 · see citing works

Combinations of muscle synergies in the construction of a natural motor behavior

d’Avella, A., Saltiel, P., & Bizzi, E. (2003) · Nature Neuroscience · doi:10.1038/nn1010 · Cited by 1,348 · see citing works

Spectral grouping using the nystrom method

Fowlkes, C. C., Belongie, S., Chung, F., et al. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262185 · Cited by 1,346 · see citing works

MetaCost

Domingos, P. (1999) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312220 · Cited by 1,309 · see citing works

Head Pose Estimation in Computer Vision: A Survey

Murphy-Chutorian, E., & Trivedi, M. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.106 · Cited by 1,304 · see citing works

Soft Margins for AdaBoost

Rätsch, G., Onoda, T., & Müller, K. R. (2001) · Machine Learning · doi:10.1023/a:1007618119488 · Cited by 1,302 · see citing works

Efficient BackProp

LeCun, Y., Bottou, L., Orr, G., & Müller, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_3 · Cited by 1,268 · see citing works

Machine learning for data-driven discovery in solid Earth geoscience

Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019) · Science · doi:10.1126/science.aau0323 · Cited by 1,271 · see citing works

The principles of software QRS detection

Kohler, B.-U., Hennig, C., & Orglmeister, R. (2002) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.993193 · Cited by 1,248 · see citing works

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

Lessmann, S., Baesens, B., Mues, C., & Pietsch, S. (2008) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2008.35 · Cited by 1,231 · see citing works

A review of methods for spike sorting: the detection and classification of neural action potentials

Lewicki, M. S. (1998) · Network Computation in Neural Systems · doi:10.1088/0954-898x_9_4_001 · Cited by 1,372 · see citing works

Online selection of discriminative tracking features

Collins, R. T., Liu, Y., & Leordeanu, M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.205 · Cited by 1,211 · see citing works

Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

Zhang, M.-L., & Zhou, Z. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.162 · Cited by 1,188 · see citing works

Statistical Physics of Spin Glasses and Information Processing

Nishimori, H. (2001) · doi:10.1093/acprof:oso/9780198509417.001.0001 · Cited by 1,408 · see citing works

Data-Driven Science and Engineering

Brunton, S. L., & Kutz, J. N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108380690 · Cited by 1,177 · see citing works

A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

Lim, T.-S., Loh, W., & Shih, Y. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,133 · see citing works

Convolutional neural networks for large-scale remote-sensing image classification

Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2612821 · Cited by 1,114 · see citing works

A Survey of Dimension Reduction Techniques

Fodor, I. K. (2002) · doi:10.2172/15002155 · Cited by 1,109 · see citing works

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

Unke, O. T., & Meuwly, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00181 · Cited by 1,071 · see citing works

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Gerónimo, D., López, A. M., Sappa, A. D., & Graf, T. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.122 · Cited by 971 · see citing works

ECG analysis: a new approach in human identification

Biel, L., Pettersson, O., Philipson, L., et al. (2001) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.930458 · Cited by 950 · see citing works

Length-dependent prediction of protein intrinsic disorder

Peng, K., Predrag Radivojac, A., Vučetić, S., Dunker, A. K., & Zoran Obradovic, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-208 · Cited by 926 · see citing works

Detection, classification, and tracking of targets

Li, D., Wong, K. D., Hu, Y. H., & Sayeed, A. M. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.985674 · Cited by 934 · see citing works

Multilayer Perceptron (MLP)

Taud, H., & Mas, J. (2017) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-319-60801-3_27 · Cited by 930 · see citing works

Efficient BackProp

LeCun, Y., Bottou, L., Orr, G., & Müller, K. R. (1998) · Lecture notes in computer science · doi:10.1007/3-540-49430-8_2 · Cited by 920 · see citing works

The Nature Of Statistical Learning Theory~

Cherkassky, V. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1997.641482 · Cited by 919 · see citing works

Benchmarking state-of-the-art classification algorithms for credit scoring

Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904 · see citing works

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

Gromski, P. S., Muhamadali, H., Ellis, D. I., Yun Xu, A., Correa, E. S., Turner, M. L., & Goodacre, R. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2015.02.012 · Cited by 914 · see citing works

Empirical validation of object-oriented metrics on open source software for fault prediction

Gyimóthy, T., Ferenć, R., & Siket, I. (2005) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2005.112 · Cited by 904 · see citing works

Artificial neural networks as a tool in ecological modelling, an introduction

Lek, S., & Guégan, J. (1999) · Ecological Modelling · doi:10.1016/s0304-3800(99)00092-7 · Cited by 891 · see citing works

Hydrological modelling using artificial neural networks

Dawson, C. W., & Wilby, R. L. (2001) · Progress in Physical Geography Earth and Environment · doi:10.1177/030913330102500104 · Cited by 904 · see citing works

A Survey of Statistical Network Models

Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 815 · see citing works

What Are Artificial Neural Networks?

Krogh, A. (2008) · Nature Biotechnology · doi:10.1038/nbt1386 · Cited by 844 · see citing works

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849 · see citing works

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

Karlaftis, M. G., & Vlahogianni, E. I. (2010) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2010.10.004 · Cited by 843 · see citing works

Safety Verification of Deep Neural Networks

Huang, X., Kwiatkowska, M., Wang, S., & Wu, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63387-9_1 · Cited by 854 · see citing works

Extracting semantic representations from word co-occurrence statistics: A computational study

Bullinaria, J. A., & Levy, J. P. (2007) · Behavior Research Methods · doi:10.3758/bf03193020 · Cited by 823 · see citing works

Semi-Supervised Self-Training of Object Detection Models

Rosenberg, C., Hebert, M., & Schneiderman, H. (2005) · 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1 · doi:10.1109/acvmot.2005.107 · Cited by 796 · see citing works

An Empirical Comparison of Machine Learning Models for Time Series Forecasting

Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El‐Shishiny, H. (2010) · Econometric Reviews · doi:10.1080/07474938.2010.481556 · Cited by 795 · see citing works

Formal theory of creativity, fun, and intrinsic motivation (1990–2010)

Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747 · see citing works

Pattern classification with missing data: a review

García-Laencina, P. J., Sancho‐Gómez, J., & Figueiras-Vidal, A. R. (2009) · Neural Computing and Applications · doi:10.1007/s00521-009-0295-6 · Cited by 766 · see citing works

OP-ELM: Optimally Pruned Extreme Learning Machine

Miché, Y., Antti Sorjamaa, B., Bas, P., Simula, O., Christian Jutten, B., & Lendasse, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2036259 · Cited by 765 · see citing works

Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

Khosravi, A., Nahavandi, S., Creighton, D., et al. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2096824 · Cited by 765 · see citing works

neuralnet: Training of Neural Networks

Günther, F., & Fritsch, S. G. (2010) · The R Journal · doi:10.32614/rj-2010-006 · Cited by 761 · see citing works

The 2005 PASCAL Visual Object Classes Challenge

Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science · doi:10.1007/11736790_8 · Cited by 765 · see citing works

Deep Learning in Medical Image Analysis

Chan, H., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-33128-3_1 · Cited by 783 · see citing works

Benchmarking Least Squares Support Vector Machine Classifiers

Gestel, T. V., Suykens, J. A. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., Moor, B. D., & Vandewalle, J. P. L. (2003) · Machine Learning · doi:10.1023/b:mach.0000008082.80494.e0 · Cited by 746 · see citing works

Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis

Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2017939 · Cited by 747 · see citing works

Cortical rewiring and information storage

Chklovskii, D. B., Mel, B. W., & Svoboda, K. (2004) · Nature · doi:10.1038/nature03012 · Cited by 717 · see citing works

An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

Di, Q., Amini, H., Shi, L., Kloog, I., Silvern, R. F., Kelly, J. T., Sabath, M. B., Choirat, C., Koutrakis, P., Lyapustin, A., et al. (2019) · Environment International · doi:10.1016/j.envint.2019.104909 · Cited by 740 · see citing works

Recovering 3D human pose from monocular images

Agarwal, A., & Triggs, B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.21 · Cited by 727 · see citing works

Nota sobre o problema da generalização

Costa, E. J. F., & Kodratoff, Y. (2014) · Psicologia · doi:10.17575/rpsicol.v4i1/2.871

Evaluation of a Congregational School's Principal

Hillelsohn, M. J. (1981) · Jewish Education · doi:10.1080/15244118108547932

The New Segregation and Generalization of IMO42-2

Kai-ting, W. (2005) · Journal of Bijie Teachers College

Introduction to the Special Issue: Toward an Explicit Technology for Generalizing Academic Behavior

Codding, R. S., & Poncy, B. C. (2010) · Journal of Behavioral Education · doi:10.1007/s10864-010-9098-1 · Cited by 14 · see citing works

Behavior therapy: Problems in Generalization

Gruber, R. P. (1971) · Behavior Therapy · doi:10.1016/s0005-7894(71)80070-9 · Cited by 19 · see citing works