Overview
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.
This book title, Learning Kernel Classifiers (Theory and Algorithms), ISBN: 9780262546591, by Ralf Herbrich, published by MIT Press (November 1, 2022) is available in paperback. Our minimum order quantity is 25 copies. All standard bulk book orders ship FREE in the continental USA and delivered in 4-10 business days.
Unlike Amazon and other retailers who may also offer Learning Kernel Classifiers (Theory and Algorithms) books on their website, we specialize in large quantities and provide personal service, from trusted, experienced, friendly people in Portland, Oregon. We offer a Price Match Guarantee, and QuickQuote form, to make purchasing quick and easy.
Prefer to work with a human being when you order Learning Kernel Classifiers (Theory and Algorithms) books in bulk? Our Book Specialists are standing by Monday-Friday 8-5 PST, ready to help!