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Learning Kernel Classifiers

Librairie Eyrolles - Paris 5e
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Learning Kernel Classifiers

Learning Kernel Classifiers

Theory and algorithms

364 pages, parution le 30/09/2002

Résumé

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.

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

Caractéristiques techniques du livre "Learning Kernel Classifiers"

  PAPIER
Éditeur(s) The MIT Press
Auteur(s) Ralf Herbrich
Parution 30/09/2002
Nb. de pages 364
Format 18,5 x 23,5
Couverture Relié
Poids 870g
Intérieur Noir et Blanc
EAN13 9780262083065
ISBN13 978-0-262-08306-5
Sélection de Noël

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