Vladimir Vapnik

Isabelle Guyon, Harris Drucker, Yann LeCun, 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

Model Selection for Small Sample Regression

Yoshua Bengio, Olivier Chapelle, Vladimir Vapnik, 2002, Machine Learning.

Learning with Rigorous Support Vector Machines

Jinbo Bi, Vladimir Vapnik, V. Vapnik, 2003, COLT.

Incorporating Invariances in Support Vector Learning Machines

Bernhard Schölkopf, Christopher J. C. Burges, Vladimir Vapnik, 1996, ICANN.

Principles of Risk Minimization for Learning Theory

Vladimir Vapnik, V. Vapnik, 1991, NIPS.

Support-vector networks

Vladimir Vapnik, Corinna Cortes, V. Vapnik, 2004, Machine Learning.

Large margin vs. large volume in transductive learning

Ran El-Yaniv, Vladimir Vapnik, Dmitry Pechyony, 2008, Machine Learning.

Support Vector Clustering

Hava T. Siegelmann, David Horn, Asa Ben-Hur, 2002, J. Mach. Learn. Res..

A new learning paradigm: Learning using privileged information

Vladimir Vapnik, Akshay Vashist, V. Vapnik, 2009, Neural Networks.

Local Learning Algorithms

Léon Bottou, Vladimir Vapnik, L. Bottou, 1992, Neural Computation.

Learning Curves: Asymptotic Values and Rate of Convergence

Lawrence D. Jackel, Corinna Cortes, Sara A. Solla, 1993, NIPS.

Learning using hidden information (Learning with teacher)

Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch, 2009, 2009 International Joint Conference on Neural Networks.

Large Margin vs. Large Volume in Transductive Learning

Ran El-Yaniv, Vladimir Vapnik, Dmitry Pechyony, 2008, ECML/PKDD.

A Support Vector Method for Clustering

Hava T. Siegelmann, David Horn, Vladimir Vapnik, 2000, NIPS.

Multidimensional splines with infinite number of knots as SVM kernels

Rauf Izmailov, Vladimir Vapnik, Akshay Vashist, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

On the Theory of Learnining with Privileged Information

Vladimir Vapnik, Dmitry Pechyony, V. Vapnik, 2010, NIPS.

A Case Study in Handwritten Digit Recognition

Harris Drucker, Patrice Y. Simard, Isabelle Guyon, 1994 .

Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities

Vladimir Vapnik, V. Vapnik, 1971 .

The Support Vector Method

Vladimir Vapnik, V. Vapnik, 1997, ICANN.

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

Harris Drucker, Corinna Cortes, Vladimir Vapnik, 1995, KDD.

Statistical learning theory

Vladimir Vapnik, V. Vapnik, 1998 .

Transductive Inference for Estimating Values of Functions

Jason Weston, Olivier Chapelle, Vladimir Vapnik, 1999, NIPS.

Methods of Function Estimation

Vladimir Vapnik, 2000 .

Rethinking statistical learning theory: learning using statistical invariants

Rauf Izmailov, Vladimir Vapnik, V. Vapnik, 2018, Machine Learning.

Learning with Intelligent Teacher

Rauf Izmailov, Vladimir Vapnik, 2016, COPA.

An overview of statistical learning theory

Vladimir Vapnik, V. Vapnik, 1999, IEEE Trans. Neural Networks.

Learning hidden information: SVM+

Vladimir Vapnik, 2006, 2006 IEEE International Conference on Granular Computing.

Feature Selection for SVMs

Sayan Mukherjee, Jason Weston, Tomaso A. Poggio, 2000, NIPS.

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

Rauf Izmailov, Vladimir Vapnik, 2015, SLDS.

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

Rauf Izmailov, Vladimir Vapnik, 2015, SLDS.

Estimation of dependencies based on small number of observations

Vladimir Vapnik, 1995, Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr).

Parallel Support Vector Machines: The Cascade SVM

Igor Durdanovic, Léon Bottou, Hans Peter Graf, 2004, NIPS.

Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression

Alexander Gammerman, Vladimir Vovk, Ilia Nouretdinov, 2011, NeuroImage.

Methods of Parametric Statistics for the Pattern Recognition Problem

Vladimir Vapnik, V. Vapnik, 2006 .

Support Vector Regression Machines

Alexander J. Smola, Harris Drucker, Linda Kaufman, 1996, NIPS.

Methods of Expected-Risk Minimization

Vladimir Vapnik, V. Vapnik, 2006 .

Prior Knowledge in Support Vector Kernels

Bernhard Schölkopf, Alexander J. Smola, Patrice Y. Simard, 1997, NIPS.

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

Alexander J. Smola, Steven E. Golowich, Vladimir Vapnik, 1996, NIPS.

Kernel Dependency Estimation

Bernhard Schölkopf, Jason Weston, André Elisseeff, 2002, NIPS.

Boosting and Other Ensemble Methods

Harris Drucker, Lawrence D. Jackel, Yann LeCun, 1994, Neural Computation.

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Bernhard Schölkopf, Tomaso A. Poggio, Federico Girosi, 1997, IEEE Trans. Signal Process..

Learning by Transduction

Alexander Gammerman, Vladimir Vovk, Vladimir Vapnik, 1998, UAI.

Gene Selection for Cancer Classification using Support Vector Machines

Jason Weston, Isabelle Guyon, Vladimir Vapnik, 2002, Machine Learning.

Support vector machines for spam categorization

Harris Drucker, Vladimir Vapnik, Donghui Wu, 1999, IEEE Trans. Neural Networks.

A Method of Minimizing Empirical Risk for the Problem of Pattern Recognition

Vladimir Vapnik, V. Vapnik, 2006 .

Learning algorithms for classification: A comparison on handwritten digit recognition

Harris Drucker, Lawrence D. Jackel, Patrice Y. Simard, 1995 .

Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)

Vladimir Vapnik, S. Kotz, V. Vapnik, 2006 .

Discovering Informative Patterns and Data Cleaning

Isabelle Guyon, Nada Matic, Vladimir Vapnik, 1996, Advances in Knowledge Discovery and Data Mining.

Knowledge transfer in SVM and neural networks

Rauf Izmailov, Vladimir Vapnik, V. Vapnik, 2017, Annals of Mathematics and Artificial Intelligence.

Support Vector Method for Multivariate Density Estimation

Sayan Mukherjee, Vladimir Vapnik, 1999, NIPS.

Complete statistical theory of learning: learning using statistical invariants

Rauf Izmailov, Vladimir Vapnik, V. Vapnik, 2020, COPA.

Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models

Bernhard Schölkopf, Heinrich H. Bülthoff, Thomas Vetter, 1996, ICANN.

Model complexity control for regression using VC generalization bounds

Vladimir Cherkassky, Vladimir Vapnik, Xuhui Shao, 1999, IEEE Trans. Neural Networks.

Learning using hidden information: Master-class learning

Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch, 2007, NATO ASI Mining Massive Data Sets for Security.

SMO-Style Algorithms for Learning Using Privileged Information

Rauf Izmailov, Vladimir Vapnik, Akshay Vashist, 2010, DMIN.

Synergy of Monotonic Rules

Rauf Izmailov, Vladimir Vapnik, V. Vapnik, 2016, J. Mach. Learn. Res..

A support vector clustering method

Hava T. Siegelmann, David Horn, Vladimir Vapnik, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

Using support vector machines for time series prediction

Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, 1999 .

Inductive principles of the search for empirical dependences (methods based on weak convergence of probability measures)

Vladimir Vapnik, V. Vapnik, 1989, COLT '89.

A Constructive Setting for the Problem of Density Ratio Estimation

Rauf Izmailov, Vladimir Vapnik, Igor Braga, 2014, SDM.

Structural Risk Minimization for Character Recognition

Isabelle Guyon, Léon Bottou, Bernhard E. Boser, 1991, NIPS.

Measuring the VC-Dimension of a Learning Machine

Yann LeCun, Vladimir Vapnik, Esther Levin, 1994, Neural Computation.

The Boosting Approach to Machine Learning An Overview

Harris Drucker, Lawrence D. Jackel, Yann LeCun, 2003 .

Predicting Time Series with Support Vector Machines

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1997, ICANN.

Model Selection for Support Vector Machines

Olivier Chapelle, Vladimir Vapnik, O. Chapelle, 1999, NIPS.

Multivariate Density Estimation: a Support Vector Machine Approach

Vladimir Vapnik, Sayan D. Mukherjee, V. Vapnik, 1999 .

Local Algorithms for Pattern Recognition and Dependencies Estimation

Léon Bottou, Vladimir Vapnik, L. Bottou, 1993, Neural Computation.

Inference with the Universum

Jason Weston, Léon Bottou, Ronan Collobert, 2006, ICML.

Vicinal Risk Minimization

Jason Weston, Léon Bottou, Olivier Chapelle, 2000, NIPS.

Support vector machines for histogram-based image classification

Patrick Haffner, Olivier Chapelle, Vladimir Vapnik, 1999, IEEE Trans. Neural Networks.

Choosing Multiple Parameters for Support Vector Machines

Sayan Mukherjee, Olivier Chapelle, Vladimir Vapnik, 2002, Machine Learning.

Comparison of learning algorithms for handwritten digit recognition

Harris Drucker, Lawrence D. Jackel, Patrice Y. Simard, 1995 .

Automatic Capacity Tuning of Very Large VC-Dimension Classifiers

Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik, 1992, NIPS.

Solution of Ill-posed Problems. Interpretation of Measurements Using the Method of Structural Risk Minimization

Vladimir Vapnik, V. Vapnik, 2006 .

Reinforced SVM method and memorization mechanisms

Rauf Izmailov, Vladimir Vapnik, V. Vapnik, 2021, Pattern Recognit..

Falsificationism and Statistical Learning Theory: Comparing the Popper and Vapnik-Chervonenkis Dimensions

Bernhard Schölkopf, David Corfield, Vladimir Vapnik, 2009 .