Gaussian Processes for Machine Learning

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

ISBN 026218253X   Pages 266 ISBN13 9780262182539 (What's this?)   Volumes 1 Publisher MIT Press Ltd   Weight (grammes) 749 Imprint MIT Press   Published in Cambridge, Mass. Format Hardback   Series title Adaptive Computation and Machine Learning Series Publication date 10 Jan 2006   Height (mm) 254 Library of Congress 2005053433   Width (mm) 203 DEWEY 006.31015192   Spine width (mm) 19 DEWEY edition DC22   Academic level Professional / Scholarly

1		Introduction		1
2		Regression		7
3		Classification		33
4		Covariance functions		79
5		Model selection and adaptation of hyperparameters		105
6		Relationships between GPs and other models		129
7		Theoretical perspectives		151
8		Approximation methods for large datasets		171
9		Further issues and conclusions		189

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