John Shawe‐Taylor

Kernel Methods for Pattern Analysis

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and lo...

An Introduction to Support Vector Machines

This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Th...

On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum

PAC Bayes and Margins

Spectral Kernel Methods for Clustering

In this paper we introduce new algorithms for unsupervised learning based on the use of a kernel matrix. All the information required by such algorithms is contained in the eige...

Structural risk minimization over data-dependent hierarchies

The paper introduces some generalizations of Vapnik's (1982) method of structural risk minimization (SRM). As well as making explicit some of the details on SRM, it provides a r...

Generalization Performance of Support Vector Machines and Other Pattern Classifiers

On the generalization of soft margin algorithms

Generalization bounds depending on the margin of a classifier are a relatively new development. They provide an explanation of the performance of state-of-the-art learning syste...

A result of Vapnik with applications