Learning Kernel Classifiers : Theory and Algorithms

Ralf Herbrich

ISBN 10: 0262546590 / ISBN 13: 9780262546591

Published by MIT Press, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - An overview of the theory and application of kernel classification methods.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.

Seller Inventory # 9780262546591

Bibliographic Details

• Title: Learning Kernel Classifiers : Theory and Algorithms
• Publisher: MIT Press
• Publication Date: 2022
• Language: English
• ISBN 10: 0262546590
• ISBN 13: 9780262546591
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 178H x 22D millimeters 229 millimeters width by 178 millimeters height by 22 millimeters depth
• Weight: 663 grams

About this title

Synopsis

An overview of the theory and application of kernel classification methods.

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.