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Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning) by Ralf Herbrich is a nonfiction available to read on EtoBox.

What is Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning) about?

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

Who reads Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)?

Author

Ralf Herbrich

Publisher

THE MIT PRESS

Published

2002

Language

EN

ISBN

9780521437141

Category

nonfiction

Subjects

Science, Mathematics, Programming

Rating

4.14 / 5 (188 ratings)

Updated

2026-03-14