Vladimir Vapnik

According to our database, Vladimir Vapnik authored at least 82 papers between 1989 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2021

Reinforced SVM method and memorization mechanisms.

Vladimir Vapnik, Rauf Izmailov

Pattern Recognit., 2021

2020

Complete statistical theory of learning: learning using statistical invariants.

Vladimir Vapnik, Rauf Izmailov

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2020

2019

Rethinking statistical learning theory: learning using statistical invariants.

Vladimir Vapnik, Rauf Izmailov

Mach. Learn., 2019

Complete Statistical Theory of Learning.

Vladimir Naumovich Vapnik

Autom. Remote. Control., 2019

2017

Knowledge transfer in SVM and neural networks.

Vladimir Vapnik, Rauf Izmailov

Ann. Math. Artif. Intell., 2017

2016

Synergy of Monotonic Rules.

Vladimir Vapnik, Rauf Izmailov

J. Mach. Learn. Res., 2016

Unifying distillation and privileged information.

David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik

Proceedings of the 4th International Conference on Learning Representations, 2016

Learning with Intelligent Teacher.

Vladimir Vapnik, Rauf Izmailov

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2016

2015

Constructive setting for problems of density ratio estimation.

Vladimir Vapnik, Igor Braga, Rauf Izmailov

Stat. Anal. Data Min., 2015

Learning using privileged information: similarity control and knowledge transfer.

Vladimir Vapnik, Rauf Izmailov

J. Mach. Learn. Res., 2015

V-matrix method of solving statistical inference problems.

Vladimir Vapnik, Rauf Izmailov

J. Mach. Learn. Res., 2015

Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis.

Vladimir Vapnik, Rauf Izmailov

Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015

Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis.

Vladimir Vapnik, Rauf Izmailov

Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015

2014

A Constructive Setting for the Problem of Density Ratio Estimation.

Vladimir Vapnik, Igor Braga, Rauf Izmailov

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

2013

Multidimensional splines with infinite number of knots as SVM kernels.

Rauf Izmailov, Vladimir Vapnik, Akshay Vashist

Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities.

Vladimir Naumovich Vapnik, Alexey Ya. Chervonenkis

Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2011

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

2010

On the Theory of Learnining with Privileged Information.

Dmitry Pechyony, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

SMO-Style Algorithms for Learning Using Privileged Information.

Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik

Proceedings of The 2010 International Conference on Data Mining, 2010

2009

A new learning paradigm: Learning using privileged information.

Vladimir Vapnik, Akshay Vashist

Neural Networks, 2009

Learning using hidden information (Learning with teacher).

Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch

Proceedings of the International Joint Conference on Neural Networks, 2009

2008

Large margin vs. large volume in transductive learning.

Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik

Mach. Learn., 2008

2007

Learning using hidden information: Master-class learning.

Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch

Proceedings of the Mining Massive Data Sets for Security, 2007

2006

Inference with the Universum.

Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik

Proceedings of the Machine Learning, 2006

Learning hidden information: SVM+.

Vladimir Vapnik

Proceedings of the 2006 IEEE International Conference on Granular Computing, 2006

Estimation of Dependences Based on Empirical Data, Second Editiontion

Vladimir Vapnik

Springer, ISBN: 978-0-387-30865-4, 2006

Transductive Inference and Semi-Supervised Learning.

Vladimir Vapnik

Proceedings of the Semi-Supervised Learning, 2006

2004

Parallel Support Vector Machines: The Cascade SVM.

Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

Learning with Rigorous Support Vector Machines.

Jinbo Bi, Vladimir Vapnik

Proceedings of the Computational Learning Theory and Kernel Machines, 2003

2002

Gene Selection for Cancer Classification using Support Vector Machines.

Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik

Mach. Learn., 2002

Choosing Multiple Parameters for Support Vector Machines.

Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee

Mach. Learn., 2002

Model Selection for Small Sample Regression.

Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio

Mach. Learn., 2002

Kernel Dependency Estimation.

Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Support Vector Clustering.

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik

J. Mach. Learn. Res., 2001

2000

Bounds on Error Expectation for Support Vector Machines.

Vladimir Vapnik, Olivier Chapelle

Neural Comput., 2000

Feature Selection for SVMs.

Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Vicinal Risk Minimization.

Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

A Support Vector Method for Clustering.

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

SVM method of estimating density, conditional probability, and conditional density.

Vladimir Naumovich Vapnik

Proceedings of the IEEE International Symposium on Circuits and Systems, 2000

A Support Vector Clustering Method.

Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik

Proceedings of the 15th International Conference on Pattern Recognition, 2000

The Nature of Statistical Learning Theory

Vladimir Vapnik

Statistics for Engineering and Information Science, Springer, ISBN: 978-1-4757-3264-1, 2000

The Nature of Statistical Learning Theory, Second Edition.

Vladimir Naumovich Vapnik

Statistics for Engineering and Information Science, Springer, ISBN: 978-0-387-98780-4, 2000

1999

An overview of statistical learning theory.

Vladimir Vapnik

IEEE Trans. Neural Networks, 1999

Support vector machines for spam categorization.

Harris Drucker, Donghui Wu, 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 histogram-based image classification.

Olivier Chapelle, Patrick Haffner, Vladimir Vapnik

IEEE Trans. Neural Networks, 1999

Support Vector Method for Multivariate Density Estimation.

Vladimir Vapnik, Sayan Mukherjee

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Transductive Inference for Estimating Values of Functions.

Olivier Chapelle, Vladimir Vapnik, Jason Weston

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Model Selection for Support Vector Machines.

Olivier Chapelle, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

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

Learning by Transduction.

Alexander Gammerman, Volodya Vovk, Vladimir Vapnik

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

Statistical learning theory.

Vladimir Vapnik

Wiley, ISBN: 978-0-471-03003-4, 1998

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

Prior Knowledge in Support Vector Kernels.

Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

The Support Vector Method.

Vladimir Vapnik

Proceedings of the Artificial Neural Networks, 1997

Predicting Time Series with Support Vector Machines.

Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik

Proceedings of the Artificial Neural Networks, 1997

1996

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing.

Vladimir Vapnik, Steven E. Golowich, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Support Vector Regression Machines.

Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Statistical Theory of Generalization (Abstract).

Vladimir Vapnik

Proceedings of the Machine Learning, 1996

Incorporating Invariances in Support Vector Learning Machines.

Bernhard Schölkopf, Chris Burges, Vladimir Vapnik

Proceedings of the Artificial Neural Networks, 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

Proceedings of the Artificial Neural Networks, 1996

Discovering Informative Patterns and Data Cleaning.

Isabelle Guyon, Nada Matic, Vladimir Vapnik

Proceedings of the Advances in Knowledge Discovery and Data Mining., 1996

1995

Support-Vector Networks.

Corinna Cortes, Vladimir Vapnik

Mach. Learn., 1995

Extracting Support Data for a Given Task.

Bernhard Schölkopf, Chris Burges, Vladimir Vapnik

Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995

Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates.

Corinna Cortes, Harris Drucker, Dennis Hoover, Vladimir Vapnik

Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995

Estimation of dependencies based on small number of observations.

Vladimir Vapnik

Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, 1995

1994

Measuring the VC-Dimension of a Learning Machine.

Vladimir Vapnik, Esther Levin, Yann LeCun

Neural Comput., 1994

Boosting and Other Ensemble Methods.

Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik

Neural Comput., 1994

Discovering Informative Patterns and Data Cleaning.

Isabelle Guyon, Nada Matic, Vladimir Vapnik

Proceedings of the Knowledge Discovery in Databases: Papers from the 1994 AAAI Workshop, 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

Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994

Boosting and Other Machine Learning Algorithms.

Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik

Proceedings of the Machine Learning, 1994

1993

Local Algorithms for Pattern Recognition and Dependencies Estimation.

Vladimir Vapnik, Léon Bottou

Neural Comput., 1993

Learning Curves: Asymptotic Values and Rate of Convergence.

Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker

Proceedings of the Advances in Neural Information Processing Systems 6, 1993

Writer-adaptation for on-line handwritten character recognition.

Nada Matic, Isabelle Guyon, John S. Denker, Vladimir Vapnik

Proceedings of the 2nd International Conference Document Analysis and Recognition, 1993

1992

Local Learning Algorithms.

Léon Bottou, Vladimir Vapnik

Neural Comput., 1992

Automatic Capacity Tuning of Very Large VC-Dimension Classifiers.

Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

Computer aided cleaning of large databases for character recognition.

Nada Matic, Isabelle Guyon, Léon Bottou, John S. Denker, Vladimir Vapnik

Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992

Capacity control in linear classifiers for pattern recognition.

Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla

Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992

A Training Algorithm for Optimal Margin Classifiers.

Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik

Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992

1991

Principles of Risk Minimization for Learning Theory.

Vladimir Vapnik

Proceedings of the Advances in Neural Information Processing Systems 4, 1991

Structural Risk Minimization for Character Recognition.

Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla

Proceedings of the Advances in Neural Information Processing Systems 4, 1991

1989

Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures).

Vladimir Vapnik

Proceedings of the Second Annual Workshop on Computational Learning Theory, 1989