A Probabilistic Theory of Pattern Recognition

An introduction to probability theory and its applications

(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,621

Probability theory

Loève, M. (1963) · Andalas University Repository (Andalas University) · Cited by 5,938

Probability: Theory and Examples.

Prewitt, K., & Durrett, R. T. (1992) · Journal of the American Statistical Association · doi:10.2307/2290301 · Cited by 3,238

Probability and Random Processes

Grimmett, G., & Stirzaker, D. (2004) · Elsevier eBooks · doi:10.1016/b978-0-12-172651-5.x5000-3 · Cited by 2,607

Probability Theory: Independence, Interchangeability, Martingales

Lund, R., Chow, Y. S., & Teicher, H. (1998) · Journal of the American Statistical Association · doi:10.2307/2670078 · Cited by 1,095

Real Analysis and Probability.

Ross, K. A., & ASH, R. B. (1975) · American Mathematical Monthly · doi:10.2307/2319153 · Cited by 727

Road sign classification using Laplace kernel classifier

Paclı́k, P., Novovičová, J., Pudil, P., & Somol, P. (2000) · Pattern Recognition Letters · doi:10.1016/s0167-8655(00)00078-7 · Cited by 152

Ranking and empirical minimization of U-statisticsPreprint

Clémençon, S., Lugosi, G., & Vayatis, N. (2006) · arXiv (Cornell University) · Cited by 152

A Neyman–Pearson Approach to Statistical Learning

Scott, C. D., & Nowak, R. D. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.856955 · Cited by 151

Supervised classification and mathematical optimization

Carrizosa, E., & Morales, D. R. (2012) · Computers & Operations Research · doi:10.1016/j.cor.2012.05.015 · Cited by 156

Inequalities for the L1 Deviation of the Empirical Distribution

Weissman, T., Ordentlich, E., Seroussi, G., Verdú, S., & Weinberger, M. J. (2003) · Cited by 150

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions

Fukumizu, K., Gretton, A., Lanckriet, G. R. G., Schölkopf, B., & Sriperumbudur, B. K. (2009) · neural information processing systems · Cited by 147

A Collaborative Training Algorithm for Distributed Learning

Predd, J. B., Kulkarni, S. R., & Poor, H. V. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2012992 · Cited by 147

A Proof of the Security of Quantum Key Distribution

Biham, E., Boyer, M., Boykin, P. O., Mor, T., & Roychowdhury, V. (2006) · Journal of Cryptology · doi:10.1007/s00145-005-0011-3 · Cited by 231

Sample Complexity of Testing the Manifold Hypothesis

Narayanan, H., & Mitter, S. K. (2010) · Cited by 145

Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization

Da, B., Gupta, A., & Ong, Y.-S. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2864345 · Cited by 144

Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches

Denœux, T., & Smets, P. (2006) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2006.877795 · Cited by 143

Musical Instrument Timbres Classification with Spectral Features

Agostini, G., Longari, M., & Pollastri, E. (2003) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/s1110865703210118 · Cited by 143

Bregman Voronoi Diagrams

Boissonnat, J., Nielsen, F., & Nock, R. (2010) · Discrete & Computational Geometry · doi:10.1007/s00454-010-9256-1 · Cited by 142

Hand gesture recognition using machine learning and the Myo armband

Benalcázar, M. E., Jaramillo, A. G., Jonathan, J., Zea, A., Paez, A., & Andaluz, V. H. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081366 · Cited by 142

ONLINE LEARNING WITH MARKOV SAMPLING

Smale, S., & Zhou, D. (2009) · Analysis and Applications · doi:10.1142/s0219530509001293 · Cited by 142

Consistency and Convergence Rates of One-Class SVMs and Related Algorithms

Vert, R., & Vert, J. (2006) · Cited by 141

Functional Classification in Hilbert Spaces

Gérard Biau, A., Bunea, F., & Wegkamp, M. H. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.847705 · Cited by 141

On the Support Vector Machine

Lin, Y. (2003) · Cited by 140

Speaker recognition with polynomial classifiers

Campbell, W. M., Assaleh, K. T., & Broun, C. C. (2002) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2002.1011533 · Cited by 140

The Modern Mathematics of Deep Learning

Berner, J., Grohs, P., Gitta Kutyniok, B., & Petersen, P. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009025096.002 · Cited by 199

The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method

Chou, C.-H., Kuo, B.-H., & Chang, F. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.1119 · Cited by 140

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

Enshaeifar, S., Zoha, A., Markides, A., Skillman, S., Acton, S. T., Elsaleh, T., Hassanpour, M., Ahrabian, A., Kenny, M., Klein, S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195605 · Cited by 139

Estimating Entropy on $m$ Bins Given Fewer Than $m$ Samples

Paninski, L. (2004) · IEEE Transactions on Information Theory · doi:10.1109/tit.2004.833360 · Cited by 139

One-pass AUC optimization

Gao, W., Wang, L., Jin, R., Shenghuo Zhu, B., & Zhou, Z. (2016) · Artificial Intelligence · doi:10.1016/j.artint.2016.03.003 · Cited by 138

Learning from dependent observations

Steinwart, I., Hush, D. R., & Scovel, C. (2008) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2008.04.001 · Cited by 138

Learning from User Feedback in Image Retrieval Systems

Vasconcelos, N. M., & Lippman, A. (1999) · Cited by 138

The true sample complexity of active learning

Balcan, M.-F., Hanneke, S., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-010-5174-y · Cited by 137

Sparse scene flow segmentation for moving object detection in urban environments

Lenz, P., Ziegler, J., Geiger, A., & Roser, M. (2011) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2011.5940558 · Cited by 136

Majority Vote of Diverse Classifiers for Late Fusion

Morvant, E., Habrard, A., & Ayache, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44415-3_16 · Cited by 138

Approximation with polynomial kernels and SVM classifiers

Zhou, D., & Jetter, K. (2006) · Advances in Computational Mathematics · doi:10.1007/s10444-004-7206-2 · Cited by 136

Who should be treated? Empirical welfare maximization methods for treatment choice

Kitagawa, T., & Tetenov, A. (2017) · doi:10.1920/wp.cem.2017.2417 · Cited by 135

Identification of combination gene sets for glioma classification.

Kim, S., Dougherty, E. R., Shmulevich, I., Hess, K. R., Hamilton, S. R., Trent, J. M., Fuller, G. N., & Zhang, W. (2002) · PubMed · Cited by 133

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers

Evgeniou, T., Pontil, M., & Elisseeff, A. (2004) · Machine Learning · doi:10.1023/b:mach.0000019805.88351.60 · Cited by 132

On the rate of convergence of fully connected deep neural network regression estimates

Köhler, M., & Langer, S. (2021) · The Annals of Statistics · doi:10.1214/20-aos2034 · Cited by 131

Process consistency for AdaBoost

Jiang, W. (2004) · The Annals of Statistics · doi:10.1214/aos/1079120128 · Cited by 131

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Buschek, D., Luca, A. D., & Alt, F. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702252 · Cited by 129

On the Performance of Clustering in Hilbert Spaces

Gérard Biau, A., Devroye, L., & Lugosi, G. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.913516 · Cited by 128

A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles

Jones, D. E., Ghandehari, H., & Facelli, J. C. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.04.025 · Cited by 130

Generalized density clusteringPreprint

Rinaldo, A. (2010) · Cited by 129

A New Discriminative Kernel from Probabilistic Models

Tsuda, K., Kawanabe, M., Rätsch, G., Sonnenburg, S., & Müller, K. (2002) · Neural Computation · doi:10.1162/08997660260293274 · Cited by 127

Locality preserving indexing for document representation

He, X., Cai, D., Liu, H., & Ma, W. (2004) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1008992.1009012 · Cited by 126

Hoeffding's inequality for uniformly ergodic Markov chains

Glynn, P. W., & Ormoneit, D. (2002) · Statistics & Probability Letters · doi:10.1016/s0167-7152(01)00158-4 · Cited by 126

Adaptive concept drift detection

Dries, A., & Rückert, U. (2009) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10054 · Cited by 126

Tunnel Hunter: Detecting application-layer tunnels with statistical fingerprinting

Dusi, M., Crotti, M., Gringoli, F., & Salgarelli, L. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.09.010 · Cited by 125

Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning

Chen, H., Chai, Z., Dogru, O., Jiang, B., & Huang, B. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3071292 · Cited by 129

Real-time hand gesture recognition using the Myo armband and muscle activity detection

Benalcázar, M. E., Motoche, C., Zea, J. A., Jaramillo, A. G., Anchundia, C. E., Zambrano, P., Segura, M., Palácios, F., & Pérez, M. (2017) · IEEE Ecuador Technical Chapters Meeting (ETCM) · doi:10.1109/etcm.2017.8247458 · Cited by 124

Bolstered error estimation

Braga-Neto, U., & Dougherty, E. R. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.08.017 · Cited by 124

Application of machine learning for advanced material prediction and design

Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123

Is the k -NN classifier in high dimensions affected by the curse of dimensionality?

Pestov, V. (2012) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2012.09.011 · Cited by 128

Algorithmic stability and sanity-check bounds for leave-one-out cross-validation

Kearns, M. J., & Ron, D. (1997) · Conference on Learning Theory · doi:10.1145/267460.267491 · Cited by 122

Orexin Neurons and the TASK of Glucosensing

Scott, M. M., Marcus, J., & Elmquist, J. K. (2006) · Neuron · doi:10.1016/j.neuron.2006.05.014 · Cited by 124

Generalized Robust Conjoint Estimation

Evgeniou, T., Boussios, C., & Zacharia, G. (2005) · Marketing Science · doi:10.1287/mksc.1040.0100 · Cited by 122

Optimized Substructure Discovery for Semi-structured Data

Abe, K., Kawasoe, S., Asai, T., Arimura, H., & Arikawa, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45681-3_1 · Cited by 122

Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks

Bartlett, P. L., Maiorov, V., & Meir, R. (1998) · Neural Computation · doi:10.1162/089976698300017016 · Cited by 121

Random-projection Ensemble Classification

Cannings, T. I., & Samworth, R. J. (2017) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12228 · Cited by 138

On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification

Gérard Biau, A., & Devroye, L. (2010) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2010.06.019 · Cited by 115

Mechanism Design via Machine Learning

Balcan, M.-F., & Blum, A. L. (2005) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.2005.50 · Cited by 120

A discriminative model for semi-supervised learning

Balcan, M.-F., & Blum, A. L. (2010) · Journal of the ACM · doi:10.1145/1706591.1706599 · Cited by 118

Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers

Mavroforakis, M., Georgiou, H., Dimitropoulos, N., Cavouras, D. A., & Theodoridis, S. (2006) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2006.03.002 · Cited by 119

Adaptive Mahalanobis Distance and $k$-Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing

Verdier, G., & Rosa, A. F. P. (2010) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2010.2065531 · Cited by 119

Approximate clustering without the approximation

Balcan, M.-F., Blum, A. L., & Gupta, A. (2009) · doi:10.5555/1496770.1496886 · Cited by 119

Artificial Intelligence and Advanced Materials

López, C. (2022) · Advanced Materials · doi:10.1002/adma.202208683 · Cited by 123

Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints

Azcona, D., Hsiao, I.-H., & Smeaton, A. F. (2019) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09234-7 · Cited by 114

Stability of Randomized Learning Algorithms

Elisseeff, A., Evgeniou, T., & Pontil, M. (2005) · Cited by 116

Comparative Study of Predictive Computational Models for Nanoparticle-Induced Cytotoxicity

Sayes, C. M., & Ivanov, I. V. (2010) · Risk Analysis · doi:10.1111/j.1539-6924.2010.01438.x · Cited by 119

Using EM to Classify Text from Labeled and Unlabeled Documents

Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. M. (1998) · doi:10.21236/ada350490 · Cited by 115

The Tradeoff Between Generative and Discriminative Classifiers

Bouchard, G., & Triggs, B. (2004) · Cited by 114

Statistical Learning Theory and ELM for Big Social Data Analysis

Oneto, L., Bisio, F., Cambria, E., & Anguita, D. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2016.2572540 · Cited by 116

Does data interpolation contradict statistical optimality?Preprint

Belkin, M., Rakhlin, A., & Tsybakov, A. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09471 · Cited by 113

DeEPs: A New Instance-Based Lazy Discovery and Classification System

Li, J., Dong, G., Ramamohanarao, K., & Wong, L. (2004) · Machine Learning · doi:10.1023/b:mach.0000011804.08528.7d · Cited by 112

Even one-dimensional mobility increases ad hoc wireless capacity

Diggavi, S. N., Grossglauser, M., & Tse, D. N. C. (2003) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2002.1023624 · Cited by 112

Neural and statistical classifiers-taxonomy and two case studies

Holmström, L., Koistinen, P., Laaksonen, J. T., & Erkki Oja, A. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554187 · Cited by 111

Consistent Binary Classification with Generalized Performance Metrics

Koyejo, S., Natarajan, N., Ravikumar, P., & Dhillon, I. S. (2014) · Cited by 111

Machine learning and BIM visualization for maintenance issue classification and enhanced data collection

McArthur, J. J., Shahbazi, N., Fok, R., Raghubar, C., Bortoluzzi, B. S. K., & An, A. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.06.007 · Cited by 114

New insights into Approximate Bayesian Computation

Gérard Biau, A., Cérou, F., & Arnaud Guyader, B. (2015) · Annales de l Institut Henri Poincaré Probabilités et Statistiques · doi:10.1214/13-aihp590 · Cited by 111

Transformer Fault Condition Prognosis Using Vibration Signals Over Cloud Environment

Bagheri, M., Zollanvari, A., & Nezhivenko, S. (2018) · IEEE Access · doi:10.1109/access.2018.2809436 · Cited by 110

Feature Selection in MLPs and SVMs Based on Maximum Output Information

Sindhwani, V., Rakshit, S., Deodhare, D., Deniz Erdogmus, B., Príncipe, J. C., & Niyogi, P. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.828772 · Cited by 109

Effect of separate sampling on classification accuracy

Esfahani, M. S., & Dougherty, E. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt662 · Cited by 115

A Novel Consistent Random Forest Framework: Bernoulli Random Forests

Wang, Y., Xia, S., Tang, Q., Wu, J. X., & Xingquan Zhu, A. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2729778 · Cited by 106

Development of multimarker panel for early detection of endometrial cancer. High diagnostic power of prolactin

Yurkovetsky, Z. R., Ta’asan, S., Skates, S. J., Rand, A., Lomakin, A., Linkov, F. Y., Marrangoni, A., Velikokhatnaya, L., Winans, M., & Gorelik, E. (2007) · Gynecologic Oncology · doi:10.1016/j.ygyno.2007.05.041 · Cited by 109

A probabilistic architecture for content-based image retrieval

Vasconcelos, N. M., & Lippman, A. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2000.855822 · Cited by 109

Introduction to Online Optimization

Bubeck, S. (2011) · Cited by 108

Nearest neighbor classification in infinite dimension

Cérou, F., & Arnaud Guyader, B. (2006) · ESAIM Probability and Statistics · doi:10.1051/ps:2006014 · Cited by 107

On Best Approximation by Ridge Functions

Maiorov, V. (1999) · Journal of Approximation Theory · doi:10.1006/jath.1998.3304 · Cited by 107

Markov chains, classifiers, and intrusion detection

Jha, S. K., Tan, K.-Y., & Maxion, R. A. (2005) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csfw.2001.930147 · Cited by 107

Generalization error bounds in semi-supervised classification under the cluster assumptionPreprint

Rigollet, P. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.math/0604233 · Cited by 107

Learning the structure of manifolds using random projections

Freund, Y., Dasgupta, S., Kabra, M., & Verma, N. (2007) · Cited by 106

Detection and Classification of DDoS Flooding Attacks on Software-Defined Networks: A Case Study for the Application of Machine Learning

Sangodoyin, A. O., Akinsolu, M. O., Pillai, P., & Grout, V. (2021) · IEEE Access · doi:10.1109/access.2021.3109490 · Cited by 106

Multinomial random forest

Bai, J., Li, Y., Li, J., Yang, X. S., Jiang, Y., & Xia, S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108331 · Cited by 106

General nonlinear framework for the analysis of gene interaction via multivariate expression arrays

Kim, S., Dougherty, E. R., Bittner, M. L., Chen, Y., Sivakumar, K., Meltzer, P. A., & Trent, J. M. (2000) · Journal of Biomedical Optics · doi:10.1117/1.1289142 · Cited by 106

Mixing strategies for density estimation

Yang, Y. (2000) · The Annals of Statistics · doi:10.1214/aos/1016120365 · Cited by 105

A Geometry-Based Band Selection Approach for Hyperspectral Image Analysis

Zhang, W., Li, X., Dou, Y., & Zhao, L. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2811046 · Cited by 105

Morphological perceptrons with competitive learning: Lattice-theoretical framework and constructive learning algorithm

Sussner, P., & Esmi, E. (2010) · Information Sciences · doi:10.1016/j.ins.2010.03.016 · Cited by 104

Concentration Inequalities

Boucheron, S., Lugosi, G., & Bousquet, O. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_9 · Cited by 104

On the rate of convergence of regularized boosting classifiers

Blanchard, G., Lugosi, G., & Vayatis, N. (2003) · doi:10.5555/945365.964298 · Cited by 102

Optimized fixed-size kernel models for large data sets

Brabanter, K. D., Brabanter, J. D., Suykens, J. A. K., & Moor, B. D. (2010) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2010.01.024 · Cited by 103

A PAC-Style Model for Learning from Labeled and Unlabeled Data

Balcan, M.-F., & Blum, A. L. (2005) · Lecture notes in computer science · doi:10.1007/11503415_8 · Cited by 103

Optimal rates for plug-in estimators of density level sets

Rigollet, P., & Vert, R. (2009) · Bernoulli · doi:10.3150/09-bej184 · Cited by 103

Learning submodular functions

Balcan, M.-F., & Harvey, N. J. A. (2011) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1993636.1993741 · Cited by 102

Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Miraftabzadeh, S. M., Longo, M., Foiadelli, F., Pasetti, M., & Igual, R. (2021) · Energies · doi:10.3390/en14164776 · Cited by 103

Learning Pattern ClassificationA Survey

(2009) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470544907.ch7 · Cited by 2

Automatic pattern recognition: a study of the probability of error

Devroye, L. (1988) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.3915 · Cited by 140

Non-parametric distance-based classification techniques and their applications

Pla, F., Radeva, P. I., & Vitria, J. M. (2008) · Pattern Analysis and Applications · doi:10.1007/s10044-008-0132-z

Statistical learning theory: a tutorial

Kulkarni, S. R., & Harman, G. H. (2011) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.179 · Cited by 51

Nonparametric pattern recognition

Kasdan, H. L. (1971) · Cited by 2