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.
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