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Gaussian Processes for Machine Learning

Af: Christopher K. I. Williams, Carl Edward Rasmussen Engelsk Hardback

Gaussian Processes for Machine Learning

Af: Christopher K. I. Williams, Carl Edward Rasmussen Engelsk Hardback
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A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

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.

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

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.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 272
ISBN-13: 9780262182539
Indbinding: Hardback
Udgave:
ISBN-10: 026218253X
Kategori: Machine learning
Udg. Dato: 23 nov 2005
Længde: 16mm
Bredde: 263mm
Højde: 212mm
Forlag: MIT Press Ltd
Oplagsdato: 23 nov 2005
Forfatter(e): Christopher K. I. Williams, Carl Edward Rasmussen
Forfatter(e) Christopher K. I. Williams, Carl Edward Rasmussen

Kategori Machine learning

ISBN-13 9780262182539

Sprog Engelsk

Indbinding Hardback

Sider 272

Udgave

Længde 16mm

Bredde 263mm

Højde 212mm

Udg. Dato 23 nov 2005

Oplagsdato 23 nov 2005

Forlag MIT Press Ltd

Kategori sammenhænge