Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning) by Ralf Herbrich (PDF)

Description

An overview of the theory and application of kernel classification methods.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.

User’s Reviews

Editorial Reviews: About the Author Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐Far removed from the vast array of ‘have a go, don’t worry about understanding the theory’ books that blight the fields of machine learning and AI, this textbook provides a solid introduction to kernels and works well alongside texts such as Vapnik’s Statistical Learning Theory.

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