Learning Kernel Classifiers

Linear classifiers in kernel spaces have emerged as a major topic within
the field of machine learning. The kernel technique takes the linear classifier--a
limited, but well-established and comprehensively studied model--and extends its
applicability to a wide range of nonlinear pattern-recognition tasks such as natural
language processing, machine vision, and biological sequence analysis. 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.