Learning Kernel Classifiers: Theory and Algorithms

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Overview

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

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Product Details

Meet the Author

Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College,University of Cambridge.

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Table of Contents

Series Foreword
Preface
1 Introduction 1
I Learning Algorithms
2 Kernel Classifiers from a Machine Learning Perspective 17
3 Kernel Classifiers from a Bayesian Perspective 73
II Learning Theory
4 Mathematical Models of Learning 115
5 Bounds for Specific Algorithms 163
III Appendices
A Theoretical Background and Basic Inequalities 199
B Proofs and Derivations - Part I 253
C Proofs and Derivations - Part II 281
D Pseudocodes 321
List of Symbols 331
References 339
Index 357
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