V. Vapnik

发表

Support vector density estimation

J. Weston, A. Gammerman, M. O. Stitson, 1999 .

A training algorithm for optimal margin classifiers

Bernhard E. Boser, Vladimir N. Vapnik, Isabelle M. Guyon, 1992, COLT '92.

Statistical learning theory

Vladimir Vapnik, V. Vapnik, 1998 .

The Nature of Statistical Learning Theory

Vladimir N. Vapnik, V. Vapnik, Vladimir Naumovich Vapni, 2000, Statistics for Engineering and Information Science.

Necessary and Sufficient Conditions for the Uniform Convergence of Means to their Expectations

V. Vapnik, A. Chervonenkis, 1982 .

Three fundamental concepts of the capacity of learning machines

V. Vapnik, 1993 .

Support-Vector Networks

Corinna Cortes, Vladimir Vapnik, Corinna Cortes, 1995, Machine Learning.

Estimation of Dependences Based on Empirical Data

V. Vapnik, 2006 .

Comparison of classifier methods: a case study in handwritten digit recognition

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).

Local Learning Algorithms

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

Structural Risk Minimization for Character Recognition

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

Local Algorithms for Pattern Recognition and Dependencies Estimation

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

Principles of Risk Minimization for Learning Theory

Vladimir Vapnik, V. Vapnik, 1991, NIPS.

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

Vladimir Vapnik, V. Vapnik, 1971 .

Learning algorithms for classification: A comparison on handwritten digit recognition

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

Comparison of learning algorithms for handwritten digit recognition

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

A Case Study in Handwritten Digit Recognition

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

Computer aided cleaning of large databases for character recognition

Isabelle Guyon, Léon Bottou, Vladimir Vapnik, 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

Capacity control in linear classifiers for pattern recognition

Isabelle Guyon, Léon Bottou, Bernhard E. Boser, 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

Learning Curves: Asymptotic Values and Rate of Convergence

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

Boosting and Other Ensemble Methods

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

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

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

Support vector regression with ANOVA decomposition kernels

J. Weston, A. Gammerman, M. O. Stitson, 1999 .

Incorporating Invariances in Support Vector Learning Machines

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

Pattern recognition using generalized portrait method

V. Vapnik, 1963 .

The Nature of Statistical Learning

Vladimir Vapnik, V. Vapnik, 1995 .

Estimation of Dependences Based on Empirical Data

V. Vapnik, 2006 .

Support vector machines for histogram-based image classification

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

Extracting Support Data for a Given Task

Bernhard Schölkopf, Christopher J. C. Burges, Vladimir Vapnik, 1995, KDD.

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

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

Measuring the VC-Dimension of a Learning Machine

Yann LeCun, Vladimir Vapnik, Esther Levin, 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..

Predicting Time Series with Support Vector Machines

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

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

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

Prior Knowledge in Support Vector Kernels

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

Automatic Capacity Tuning of Very Large VC-Dimension Classifiers

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

What Size Test Set Gives Good Error Rate Estimates?

Isabelle Guyon, Richard M. Schwartz, John Makhoul, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

The Boosting Approach to Machine Learning An Overview

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

Choosing Multiple Parameters for Support Vector Machines

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

Support Vector Regression Machines

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

Support Vector Clustering

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

Gene Selection for Cancer Classification using Support Vector Machines

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

Neural Information Processing Systems

Alexander J. Smola, Steven E. Golowich, Vladimir Vapnik, 1997, NIPS 1997.

Unifying distillation and privileged information pdf

Bernhard Schölkopf, David Lopez-Paz, Vladimir Vapnik, 2015, ICLR.

On the theory of learning with Privileged Information

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

Bounds on error expectation for SVM

O. Chapelle, V. Vapnik, 2000 .

An overview of statistical learning theory

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

Parallel Support Vector Machines: The Cascade SVM

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

Learning by Transduction

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

Kernel Dependency Estimation

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

Using support vector machines for time series prediction

Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, 1999 .

Feature Selection for SVMs

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

The Support Vector Method of Function Estimation

V. Vapnik, 1998 .

A note one class of perceptrons

V. Vapnik, V. Vapnik, 1964 .

A new learning paradigm: Learning using privileged information

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

The Support Vector Method

Vladimir Vapnik, V. Vapnik, 1997, ICANN.

Density Estimation using Support Vector Machines

Jason Weston, Alexander Gammerman, Vladimir Vovk, 1998 .

Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution

V. Vapnik, R. Izmailov, Í. Braga, 2013, 1306.0407.

Multivariate Density Estimation: an SVM Approach

V. Vapnik, S. Mukherjee, 1999 .

Model Selection for Support Vector Machines

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

Transductive Inference for Estimating Values of Functions

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

Support vector machines for spam categorization

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

Discovering Informative Patterns and Data Cleaning

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

Vicinal Risk Minimization

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

Bounds on Error Expectation for Support Vector Machines

V. Vapnik, O. Chapelle, O. Chapelle, 2000, Neural Computation.

Support Vector Learning Support Vector Learning Contents Summary 11 1 Introduction and Preliminaries 15 2 Support Vector Machines 33 4 Prior Knowledge in Support Vector Machines 99 5 Conclusion 125 a Object Databases 127 B Object Recognition Results 137

Vapnik, V. Vapnik, Obermayer, 1997 .

COMPARISONOFLEARNING ALGORITHMS FORHANDWRITTEN DIGIT RECOGNITION

L. Bottou, I. Guyon, Yann LeCun, 1995 .

Svms for Histogram-based Image Classiication List of Figures

V. Vapnik, 1999 .

Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)

Vladimir Vapnik, V. Vapnik, 1982 .

Model Selection for Small Sample Regression

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

Universal learning technology : Support vector machines

Vladimir Vapnik, V. Vapnik, 2005 .

The Nature of Statistical Learning Theory, Second Edition

Vladimir N. Vapnik, V. Vapnik, 2000, Statistics for Engineering and Information Science.

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