book announcement--Herbrich

[Jud Wolfskill]

I thought readers of the Connectionists List might be interested in this 
book.  For more information, please visit 
http://mitpress.mit.edu/026208306X/  Thank you!

Best,
Jud

Learning Kernel Classifiers
Theory and Algorithms
Ralf Herbrich

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.

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.

7 x 9, 384 pp., 0-262-08306-X

Adaptive Computation and Machine Learning series

Jud Wolfskill
Associate Publicist
MIT Press
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Cambridge, MA  02142
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617.253.1709 fax
wolfskil at mit.edu