
Learning Kernel Classifiers Ralf Herbrich The MIT Press 2002 ISBN: 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 PACBayesian 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. Partners 

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 herbrich@kernelmachines.org. 