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Learning Kernel Classifiers: Theory and Algorithms by Ralf Herbrich at Abbey's Bookshop,
Learning Kernel Classifiers: Theory and Algorithms
— —
Ralf Herbrich

Learning Kernel Classifiers

Theory and Algorithms

Ralf Herbrich

$145

Paperback

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English
MIT Press
01 November 2022
Mathematical theory of computation
Series: Adaptive Computation and Machine Learning series
An overview of the theory and application of kernel classification methods.

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.
By:  
Imprint:   MIT Press
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 178mm, 
Weight:   369g
ISBN:   9780262546591
ISBN 10:   0262546590
Series:   Adaptive Computation and Machine Learning series
Pages:   384
Publication Date:  
Recommended Age:   From 18 years
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active

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