GAUSSIAN PROCESSES FOR MACHINE LEARNING by Carl Edward Rasmussen, Christopher K. I. Williams ( 9780262182539 )

Item Details

Title:

GAUSSIAN PROCESSES FOR MACHINE LEARNING

By:

Carl Edward Rasmussen, Christopher K. I. Williams

Format:

Hardback

List price:

£48.00

Our price:

£38.40

Discount:

20% off

You save:

£9.60

ISBN 10:

026218253X

ISBN 13:

9780262182539

Availability:

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Stock:

Currently 6 available

Publisher:

MIT PRESS LTD

Pub. date:

23 November, 2005

Series:

Adaptive Computation and Machine Learning series

Pages:

272

Description:

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Synopsis:

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.

Publication:

Imprint:

MIT Press

Prizes:

Winner of Winner, 2009 DeGroot Prize for the best book in statistical

Returns:

Returnable

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