I thought readers of the Uncertainty in AI List might be interested in
these two books. For more information please visit the URLs listed below.
Learning Kernel Classifiers
Theory and Algorithms
Ralf Herbrich
http://mitpress.mit.edu/026208306X/
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., cloth 0-262-08306-X
Adaptive Computation and Machine Learning series
Bayes Nets and Graphical Causal Models in Psychology
Clark Glymour
http://mitpress.mit.edu/0262072203
In his new book, Clark Glymour provides an informal introduction to the
basic assumptions, algorithms, and techniques of causal Bayes nets and
graphical causal models in the context of psychological examples. He
demonstrates their potential as a powerful tool for guiding experimental
inquiry and for interpreting results in developmental psychology, cognitive
neuropsychology, psychometrics, social psychology, and studies of adult
judgment. Using Bayes net techniques, Glymour suggests novel experiments to
distinguish among theories of human causal learning and reanalyzes various
experimental results that have been interpreted or misinterpreted--without
the benefit of Bayes nets and graphical causal models. The capstone
illustration is an analysis of the methods used in Herrnstein and Murray's
book The Bell Curve; Glymour argues that new, more reliable methods of data
analysis, based on Bayes nets representations, would lead to very different
conclusions from those advocated by Herrnstein and Murray.
6 x 9, 250 pp., 100 illus., ISBN cloth 0-262-07220-3
A Bradford Book
Jud Wolfskill
Associate Publicist
MIT Press
5 Cambridge Center, 4th Floor
Cambridge, MA 02142
617.253.2079
617.253.1709 fax
wolfskil@mit.edu
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