Learning Kernel Classifiers
The MIT Press
2002 ISBN: 0-262-08306-X
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.
These pages are best viewed with Internet Explorer. You may encounter some problems with other Web browsers (e.g. Netscape, Konqueror) in the display of Greek letters. In the case of any further problems or additional comments about this Web site, please drop me a line at firstname.lastname@example.org.