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 [EMAIL PROTECTED]
