I thought readers of sci.stat.edu might be interested in these two
books. For more information, please visit
http://mitpress.mit.edu/0262194759/ and
http://mitpress.mit.edu/0262194759/026208306X Thank you!
Best,
Jud
Learning with Kernels
Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf and Alexander J. Smola
Learning with Kernels provides an introduction to Support Vector
Machines (SVMs) and related kernel methods. Although the book begins
with the basics, it also includes the latest research. It provides all
of the concepts necessary to enable a reader equipped with some basic
mathematical knowledge to enter the world of machine learning using
theoretically well-founded yet easy-to-use kernel algorithms and to
understand and apply the powerful algorithms that have been developed
over the last few years.
8 x 10, 632 pp., 138 illus., cloth, ISBN 0-262-19475-9
Adaptive Computation and Machine Learning series
Learning Kernel Classifiers
Theory and Algorithms
Ralf Herbrich
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.
7 x 9, 384 pp., 0-262-08306-X
Adaptive Computation and Machine Learning series
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