- Hardcover: 384 pages
- Publisher: MIT Press (22 Jan 2002)
- Language English
- ISBN-10: 026208306X
- ISBN-13: 978-0262083065
- Product Dimensions: 23.4 x 18.3 x 2.7 cm
- Amazon Bestsellers Rank: 1,769,609 in Books (See Top 100 in Books)
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Product Description
Product Description
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.
About the Author
Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.
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First Sentence
It was only a few years after the introduction of the first computer that one of man's greatest dreams seemed to be realizable-artificial intelligence. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index
It was only a few years after the introduction of the first computer that one of man's greatest dreams seemed to be realizable-artificial intelligence. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index
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2 of 11 people found the following review helpful
Fascinating
27 May 2004
Format:Hardcover
An fine introduction to statistical learning theory! While the audio book format is certainly an unorthodox choice, the breathy, Jessica Rabbit-style narration turns out to be a boon when getting to grips with algorithmic stability and PAC bounds. First rate!
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