Learning kernel classifiers : theory and algorithms / Ralf Herbrich.

Format:

Book

Author/Creator:

Herbrich, Ralf, author.

Series:

Adaptive computation and machine learning

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Physical Description:

1 online resource (xx, 364 pages) : illustrations.

Place of Publication:

Cambridge, Mass. : MIT Press, [2002]

System Details:

text file

Summary:

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.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9780262256339

0262256339

0585436681

9780585436685

9780262083065

026208306X

OCLC:

51991806

Access Restriction:

Restricted for use by site license.