Vladimir Vapnik

Publication Activity (10 Years)

Years Active: 1989-2021
Publications (10 Years): 12

Top Topics

Histogram Intersection Kernel

Knowledge Transfer

Inference Problems

Top Venues

J. Mach. Learn. Res.

Publications

Vladimir Vapnik, Rauf Izmailov
Reinforced SVM method and memorization mechanisms. Pattern Recognit. 119 (2021)
Vladimir Vapnik, Rauf Izmailov
Complete statistical theory of learning: learning using statistical invariants. COPA (2020)
Vladimir Vapnik, Rauf Izmailov
Rethinking statistical learning theory: learning using statistical invariants. Mach. Learn. 108 (3) (2019)
Vladimir Vapnik, Rauf Izmailov
Knowledge transfer in SVM and neural networks. Ann. Math. Artif. Intell. 81 (1-2) (2017)
Vladimir Vapnik, Rauf Izmailov
Learning with Intelligent Teacher. COPA (2016)
Vladimir Vapnik, Rauf Izmailov
Synergy of Monotonic Rules. J. Mach. Learn. Res. 17 (2016)
David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
Unifying distillation and privileged information. ICLR (Poster) (2016)
Vladimir Vapnik, Rauf Izmailov
V-matrix method of solving statistical inference problems. J. Mach. Learn. Res. 16 (2015)
Vladimir Vapnik, Igor Braga, Rauf Izmailov
Constructive setting for problems of density ratio estimation. Stat. Anal. Data Min. 8 (3) (2015)
Vladimir Vapnik, Rauf Izmailov
Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis. SLDS (2015)
Vladimir Vapnik, Rauf Izmailov
Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis. SLDS (2015)
Vladimir Vapnik, Rauf Izmailov
Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16 (2015)
Vladimir Vapnik, Igor Braga, Rauf Izmailov
A Constructive Setting for the Problem of Density Ratio Estimation. SDM (2014)
Rauf Izmailov, Vladimir Vapnik, Akshay Vashist
Multidimensional splines with infinite number of knots as SVM kernels. IJCNN (2013)
Alexander Gammerman, Volodya Vovk, Vladimir Vapnik
Learning by Transduction CoRR (2013)
Ilia Nouretdinov, Sergi G. Costafreda, Alexander Gammerman, Alexey Ya. Chervonenkis, Vladimir Vovk, Vladimir Vapnik, Cynthia H. Y. Fu
Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56 (2) (2011)
Dmitry Pechyony, Vladimir Vapnik
On the Theory of Learnining with Privileged Information. NIPS (2010)
Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik
SMO-Style Algorithms for Learning Using Privileged Information. DMIN (2010)
Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch
Learning using hidden information (Learning with teacher). IJCNN (2009)
Vladimir Vapnik, Akshay Vashist
A new learning paradigm: Learning using privileged information. Neural Networks 22 (5-6) (2009)
Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) (2008)
Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
Large margin vs. large volume in transductive learning. Mach. Learn. 72 (3) (2008)
Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch
Learning using hidden information: Master-class learning. NATO ASI Mining Massive Data Sets for Security (2007)
Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik
Inference with the Universum. ICML (2006)
Vladimir Vapnik
Learning hidden information: SVM+. GrC (2006)
Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik
Parallel Support Vector Machines: The Cascade SVM. NIPS (2004)
Jinbo Bi, Vladimir Vapnik
Learning with Rigorous Support Vector Machines. COLT (2003)
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
Model Selection for Small Sample Regression. Mach. Learn. 48 (1-3) (2002)
Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik
Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 46 (1-3) (2002)
Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. 46 (1-3) (2002)
Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik
Kernel Dependency Estimation. NIPS (2002)
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
Support Vector Clustering. J. Mach. Learn. Res. 2 (2001)
Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
A Support Vector Method for Clustering. NIPS (2000)
Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
A Support Vector Clustering Method. ICPR (2000)
Vladimir Vapnik, Olivier Chapelle
Bounds on Error Expectation for Support Vector Machines. Neural Comput. 12 (9) (2000)
Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik
Feature Selection for SVMs. NIPS (2000)
Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
Vicinal Risk Minimization. NIPS (2000)
Harris Drucker, Donghui Wu, Vladimir Vapnik
Support vector machines for spam categorization. IEEE Trans. Neural Networks 10 (5) (1999)
Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik
Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks 10 (5) (1999)
Olivier Chapelle, Vladimir Vapnik
Model Selection for Support Vector Machines. NIPS (1999)
Vladimir Vapnik
An overview of statistical learning theory. IEEE Trans. Neural Networks 10 (5) (1999)
Olivier Chapelle, Patrick Haffner, Vladimir Vapnik
Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks 10 (5) (1999)
Vladimir Vapnik, Sayan Mukherjee
Support Vector Method for Multivariate Density Estimation. NIPS (1999)
Olivier Chapelle, Vladimir Vapnik, Jason Weston
Transductive Inference for Estimating Values of Functions. NIPS (1999)
Alexander Gammerman, Volodya Vovk, Vladimir Vapnik
Learning by Transduction. UAI (1998)
Isabelle Guyon, John Makhoul, Richard M. Schwartz, Vladimir Vapnik
What Size Test Set Gives Good Error Rate Estimates?. IEEE Trans. Pattern Anal. Mach. Intell. 20 (1) (1998)
Vladimir Vapnik
The Support Vector Method. ICANN (1997)
Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik
Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45 (11) (1997)
Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik
Prior Knowledge in Support Vector Kernels. NIPS (1997)
Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik
Predicting Time Series with Support Vector Machines. ICANN (1997)
Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, Vladimir Vapnik
Support Vector Regression Machines. NIPS (1996)
Vladimir Vapnik
Statistical Theory of Generalization (Abstract). ICML (1996)
Vladimir Vapnik, Steven E. Golowich, Alexander J. Smola
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS (1996)
Volker Blanz, Bernhard Schölkopf, Heinrich H. Bülthoff, Chris Burges, Vladimir Vapnik, Thomas Vetter
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. ICANN (1996)
Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
Incorporating Invariances in Support Vector Learning Machines. ICANN (1996)
Corinna Cortes, Harris Drucker, Dennis Hoover, Vladimir Vapnik
Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates. KDD (1995)
Vladimir Vapnik
Estimation of dependencies based on small number of observations. CIFEr (1995)
Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
Extracting Support Data for a Given Task. KDD (1995)
Corinna Cortes, Vladimir Vapnik
Support-Vector Networks. Mach. Learn. 20 (3) (1995)
Vladimir Vapnik, Esther Levin, Yann LeCun
Measuring the VC-Dimension of a Learning Machine. Neural Comput. 6 (5) (1994)
Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
Boosting and Other Ensemble Methods. Neural Comput. 6 (6) (1994)
Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
Boosting and Other Machine Learning Algorithms. ICML (1994)
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
Comparison of classifier methods: a case study in handwritten digit recognition. ICPR (2) (1994)
Isabelle Guyon, Nada Matic, Vladimir Vapnik
Discovering Informative Patterns and Data Cleaning. KDD Workshop (1994)
Vladimir Vapnik, Léon Bottou
Local Algorithms for Pattern Recognition and Dependencies Estimation. Neural Comput. 5 (6) (1993)
Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker
Learning Curves: Asymptotic Values and Rate of Convergence. NIPS (1993)
Nada Matic, Isabelle Guyon, John S. Denker, Vladimir Vapnik
Writer-adaptation for on-line handwritten character recognition. ICDAR (1993)
Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS (1992)
Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla
Capacity control in linear classifiers for pattern recognition. ICPR (2) (1992)
Léon Bottou, Vladimir Vapnik
Local Learning Algorithms. Neural Comput. 4 (6) (1992)
Nada Matic, Isabelle Guyon, Léon Bottou, John S. Denker, Vladimir Vapnik
Computer aided cleaning of large databases for character recognition. ICPR (2) (1992)
Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik
A Training Algorithm for Optimal Margin Classifiers. COLT (1992)
Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla
Structural Risk Minimization for Character Recognition. NIPS (1991)
Vladimir Vapnik
Principles of Risk Minimization for Learning Theory. NIPS (1991)
Vladimir Vapnik
Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). COLT (1989)