Vladimir Vapnik

The Nature of Statistical Learning Theory

Support-Vector Networks

A training algorithm for optimal margin classifiers

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classif...

Gene Selection for Cancer Classification using Support Vector Machines

Discovering informative patterns and data cleaning

We present a method for discovering informative patterns from data. With this method, large databases can be reduced to only a few representative data entries. Our framework als...

What size test set gives good error rate estimates?

We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. ...

Structural Risk Minimization for Character Recognition

The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training data. This capacity is influenced by several facto...

The Nature of Statistical Learning Theory

Statistical Learning Theory

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis...

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

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial bas...

The Support Vector Method of Function Estimation