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Learning Kernel Classifiers: Theory and Algorithms

by

Learning Kernel Classifiers: Theory and Algorithms Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

An overview of the theory and application of kernel classification methods.

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.

Product Details

ISBN:
9780262083065
Author:
Herbrich, Ralf
Publisher:
Mit Press
Location:
Cambridge, Mass.
Subject:
Computer Science
Subject:
Artificial Intelligence
Subject:
Algorithms
Subject:
Machine learning
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Computers-Reference - General
Edition Description:
Includes bibliographical references and index.
Series:
Adaptive Computation and Machine Learning series Learning Kernel Classifiers
Series Volume:
106-3
Publication Date:
20011231
Binding:
HARDCOVER
Grade Level:
from 17
Language:
English
Illustrations:
Yes
Pages:
384
Dimensions:
9 x 7 in

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Product details 384 pages MIT Press - English 9780262083065 Reviews:
"Synopsis" by , An overview of the theory and application of kernel classification methods.
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