Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). - Vladimir Vapnik (COLT, 1989)

Structural Risk Minimization for Character Recognition. - Isabelle Guyon; Vladimir Vapnik; Bernhard E. Boser; Léon Bottou; Sara A. Solla (NIPS, 1991)

Principles of Risk Minimization for Learning Theory. - Vladimir Vapnik (NIPS, 1991)

A Training Algorithm for Optimal Margin Classifiers. - Bernhard E. Boser; Isabelle Guyon; Vladimir Vapnik (COLT, 1992)

Capacity control in linear classifiers for pattern recognition. - Isabelle Guyon; Vladimir Vapnik; Bernhard E. Boser; Léon Bottou; Sara A. Solla (ICPR (2), 1992)

Computer aided cleaning of large databases for character recognition. - Nada Matic; Isabelle Guyon; Léon Bottou; John S. Denker; Vladimir Vapnik (ICPR (2), 1992)

Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. - Isabelle Guyon; Bernhard E. Boser; Vladimir Vapnik (NIPS, 1992)

Local Learning Algorithms. - Léon Bottou; Vladimir Vapnik (Neural Comput., 1992)

Writer-adaptation for on-line handwritten character recognition. - Nada Matic; Isabelle Guyon; John S. Denker; Vladimir Vapnik (ICDAR, 1993)

Learning Curves: Asymptotic Values and Rate of Convergence. - Corinna Cortes; Lawrence D. Jackel; Sara A. Solla; Vladimir Vapnik; John S. Denker (NIPS, 1993)

Local Algorithms for Pattern Recognition and Dependencies Estimation. - Vladimir Vapnik; Léon Bottou (Neural Comput., 1993)

Boosting and Other Machine Learning Algorithms. - Harris Drucker; Corinna Cortes; Lawrence D. Jackel; Yann LeCun; Vladimir Vapnik (ICML, 1994)

Comparison of classifier methods: a case study in handwritten digit recognition. - Léon Bottou; Corinna Cortes; John S. Denker; Harris Drucker; Isabelle Guyon; Larry D. Jackel; Yann LeCun; Urs A. Müller; Eduard Säckinger; Patrice Y. Simard; Vladimir Vapnik (ICPR (2), 1994)

Discovering Informative Patterns and Data Cleaning. - Isabelle Guyon; Nada Matic; Vladimir Vapnik (KDD Workshop, 1994)

Boosting and Other Ensemble Methods. - Harris Drucker; Corinna Cortes; Lawrence D. Jackel; Yann LeCun; Vladimir Vapnik (Neural Comput., 1994)

Measuring the VC-Dimension of a Learning Machine. - Vladimir Vapnik; Esther Levin; Yann LeCun (Neural Comput., 1994)

Estimation of dependencies based on small number of observations. - Vladimir Vapnik (CIFEr, 1995)

Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates. - Corinna Cortes; Harris Drucker; Dennis Hoover; Vladimir Vapnik (KDD, 1995)

Extracting Support Data for a Given Task. - Bernhard Schölkopf; Chris Burges; Vladimir Vapnik (KDD, 1995)

Support-Vector Networks. - Corinna Cortes; Vladimir Vapnik (Mach. Learn., 1995)

Discovering Informative Patterns and Data Cleaning. - Isabelle Guyon; Nada Matic; Vladimir Vapnik (Advances in Knowledge Discovery and Data Mining, 1996)

Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. - Volker Blanz; Bernhard Schölkopf; Heinrich H. Bülthoff; Chris Burges; Vladimir Vapnik; Thomas Vetter (ICANN, 1996)

Incorporating Invariances in Support Vector Learning Machines. - Bernhard Schölkopf; Chris Burges; Vladimir Vapnik (ICANN, 1996)

Statistical Theory of Generalization (Abstract). - Vladimir Vapnik (ICML, 1996)

Support Vector Regression Machines. - Harris Drucker; Christopher J. C. Burges; Linda Kaufman; Alexander J. Smola; Vladimir Vapnik (NIPS, 1996)

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. - Vladimir Vapnik; Steven E. Golowich; Alexander J. Smola (NIPS, 1996)

Predicting Time Series with Support Vector Machines. - Klaus-Robert Müller; Alexander J. Smola; Gunnar Rätsch; Bernhard Schölkopf; Jens Kohlmorgen; Vladimir Vapnik (ICANN, 1997)

The Support Vector Method. - Vladimir Vapnik (ICANN, 1997)

Prior Knowledge in Support Vector Kernels. - Bernhard Schölkopf; Patrice Y. Simard; Alexander J. Smola; Vladimir Vapnik (NIPS, 1997)

Comparing support vector machines with Gaussian kernels to radial basis function classifiers. - Bernhard Schölkopf; Kah Kay Sung; Christopher J. C. Burges; Federico Girosi; Partha Niyogi; Tomaso A. Poggio; Vladimir Vapnik (IEEE Trans. Signal Process., 1997)

Statistical learning theory. - Vladimir Vapnik (Wiley, 1998)

Learning by Transduction. - Alexander Gammerman; Volodya Vovk; Vladimir Vapnik (UAI, 1998)

What Size Test Set Gives Good Error Rate Estimates?. - Isabelle Guyon; John Makhoul; Richard M. Schwartz; Vladimir Vapnik (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Model Selection for Support Vector Machines. - Olivier Chapelle; Vladimir Vapnik (NIPS, 1999)

Transductive Inference for Estimating Values of Functions. - Olivier Chapelle; Vladimir Vapnik; Jason Weston (NIPS, 1999)

Support Vector Method for Multivariate Density Estimation. - Vladimir Vapnik; Sayan Mukherjee 004 (NIPS, 1999)

Support vector machines for histogram-based image classification. - Olivier Chapelle; Patrick Haffner; Vladimir Vapnik (IEEE Trans. Neural Networks, 1999)

Model complexity control for regression using VC generalization bounds. - Vladimir Cherkassky; Xuhui Shao; Filip Mulier; Vladimir Vapnik (IEEE Trans. Neural Networks, 1999)

Support vector machines for spam categorization. - Harris Drucker; Donghui Wu; Vladimir Vapnik (IEEE Trans. Neural Networks, 1999)

An overview of statistical learning theory. - Vladimir Vapnik (IEEE Trans. Neural Networks, 1999)

The Nature of Statistical Learning Theory, Second Edition. - Vladimir Naumovich Vapnik (Springer, 2000)

The Nature of Statistical Learning Theory - Vladimir Vapnik (Springer, 2000)

A Support Vector Clustering Method. - Asa Ben-Hur; Hava T. Siegelmann; David Horn 001; Vladimir Vapnik (ICPR, 2000)

SVM method of estimating density, conditional probability, and conditional density. - Vladimir Naumovich Vapnik (ISCAS, 2000)

A Support Vector Method for Clustering. - Asa Ben-Hur; David Horn 001; Hava T. Siegelmann; Vladimir Vapnik (NIPS, 2000)

Vicinal Risk Minimization. - Olivier Chapelle; Jason Weston; Léon Bottou; Vladimir Vapnik (NIPS, 2000)

Feature Selection for SVMs. - Jason Weston; Sayan Mukherjee 001; Olivier Chapelle; Massimiliano Pontil; Tomaso A. Poggio; Vladimir Vapnik (NIPS, 2000)

Bounds on Error Expectation for Support Vector Machines. - Vladimir Vapnik; Olivier Chapelle (Neural Comput., 2000)

Support Vector Clustering. - Asa Ben-Hur; David Horn 001; Hava T. Siegelmann; Vladimir Vapnik (J. Mach. Learn. Res., 2001)

Kernel Dependency Estimation. - Jason Weston; Olivier Chapelle; André Elisseeff; Bernhard Schölkopf; Vladimir Vapnik (NIPS, 2002)

Model Selection for Small Sample Regression. - Olivier Chapelle; Vladimir Vapnik; Yoshua Bengio (Mach. Learn., 2002)

Choosing Multiple Parameters for Support Vector Machines. - Olivier Chapelle; Vladimir Vapnik; Olivier Bousquet; Sayan Mukherjee 001 (Mach. Learn., 2002)

Gene Selection for Cancer Classification using Support Vector Machines. - Isabelle Guyon; Jason Weston; Stephen Barnhill; Vladimir Vapnik (Mach. Learn., 2002)

Learning with Rigorous Support Vector Machines. - Jinbo Bi; Vladimir Vapnik (COLT, 2003)

Empirical inference problems. - Vladimir Vapnik (IJCNN, 2003)

Parallel Support Vector Machines: The Cascade SVM. - Hans Peter Graf; Eric Cosatto; Léon Bottou; Igor Durdanovic; Vladimir Vapnik (NIPS, 2004)

Transductive Inference and Semi-Supervised Learning. - Vladimir Vapnik (Semi-Supervised Learning, 2006)

Estimation of Dependences Based on Empirical Data, Second Editiontion - Vladimir Vapnik (Springer, 2006)

Learning hidden information: SVM+. - Vladimir Vapnik (GrC, 2006)

Inference with the Universum. - Jason Weston; Ronan Collobert; Fabian H. Sinz; Léon Bottou; Vladimir Vapnik (ICML, 2006)

Learning using hidden information: Master-class learning. - Vladimir Vapnik; Akshay Vashist; Natalya Pavlovitch (NATO ASI Mining Massive Data Sets for Security, 2007)

Large margin vs. large volume in transductive learning. - Ran El-Yaniv; Dmitry Pechyony; Vladimir Vapnik (Mach. Learn., 2008)

Learning using hidden information (Learning with teacher). - Vladimir Vapnik; Akshay Vashist; Natalya Pavlovitch (IJCNN, 2009)

A new learning paradigm: Learning using privileged information. - Vladimir Vapnik; Akshay Vashist (Neural Networks, 2009)

SMO-Style Algorithms for Learning Using Privileged Information. - Dmitry Pechyony; Rauf Izmailov; Akshay Vashist; Vladimir Vapnik (DMIN, 2010)

On the Theory of Learnining with Privileged Information. - Dmitry Pechyony; Vladimir Vapnik (NIPS, 2010)

Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. - Ilia Nouretdinov; Sergi G. Costafreda; Alexander Gammerman; Alexey Ya. Chervonenkis; Vladimir Vovk; Vladimir Vapnik; Cynthia H. Y. Fu (NeuroImage, 2011)

On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities. - Vladimir Naumovich Vapnik; Alexey Ya. Chervonenkis (Empirical Inference, 2013)

Multidimensional splines with infinite number of knots as SVM kernels. - Rauf Izmailov; Vladimir Vapnik; Akshay Vashist (IJCNN, 2013)

A Constructive Setting for the Problem of Density Ratio Estimation. - Vladimir Vapnik; Igor Braga; Rauf Izmailov (SDM, 2014)

Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis. - Vladimir Vapnik; Rauf Izmailov (SLDS, 2015)

Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis. - Vladimir Vapnik; Rauf Izmailov (SLDS, 2015)

V-matrix method of solving statistical inference problems. - Vladimir Vapnik; Rauf Izmailov (J. Mach. Learn. Res., 2015)

Learning using privileged information: similarity control and knowledge transfer. - Vladimir Vapnik; Rauf Izmailov (J. Mach. Learn. Res., 2015)

Constructive setting for problems of density ratio estimation. - Vladimir Vapnik; Igor Braga; Rauf Izmailov (Stat. Anal. Data Min., 2015)

Learning with Intelligent Teacher. - Vladimir Vapnik; Rauf Izmailov (COPA, 2016)

Unifying distillation and privileged information. - David Lopez-Paz; Léon Bottou; Bernhard Schölkopf; Vladimir Vapnik (ICLR (Poster), 2016)

Synergy of Monotonic Rules. - Vladimir Vapnik; Rauf Izmailov (J. Mach. Learn. Res., 2016)

Knowledge transfer in SVM and neural networks. - Vladimir Vapnik; Rauf Izmailov (Ann. Math. Artif. Intell., 2017)

Complete Statistical Theory of Learning. - Vladimir Naumovich Vapnik (Autom. Remote. Control., 2019)

Rethinking statistical learning theory: learning using statistical invariants. - Vladimir Vapnik; Rauf Izmailov (Mach. Learn., 2019)

Complete statistical theory of learning: learning using statistical invariants. - Vladimir Vapnik; Rauf Izmailov (COPA, 2020)

Reinforced SVM method and memorization mechanisms. - Vladimir Vapnik; Rauf Izmailov (Pattern Recognit., 2021)