Details

Title: Learning kernel classifiers: theory and algorithms
Creators: Herbrich Ralf
Organization: IEEE Xplore (Online Service); MIT Press
Imprint: Cambridge, Massachusetts London, England: MIT Press, 2002
Collection: Электронные книги зарубежных издательств; MIT Press eBooks Library; Общая коллекция
Subjects: Алгоритмы; машинное обучение
UDC: 004.421; 004.62
Language: English
Rights: Доступ по паролю из сети Интернет (чтение, печать)

Allowed Actions: View

Annotation

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.

Usage statistics

stat Access count: 2
Last 30 days: 0
Detailed usage statistics