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Gaussian processes for machine learning

Author: Carl Edward Rasmussen; Christopher K I Williams
Publisher: Cambridge, Mass. : MIT Press, ©2006.
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"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  Read more...
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Genre/Form: Electronic books
Electronic book
Additional Physical Format: Print version:
Rasmussen, Carl Edward.
Gaussian processes for machine learning.
Cambridge, Mass. : MIT Press, ©2006
(DLC) 2005053433
(OCoLC)61285753
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Carl Edward Rasmussen; Christopher K I Williams
ISBN: 9780262256834 0262256835 1423769902 9781423769903 9780262182539 026218253X
OCLC Number: 68194203
Awards: Winner of Winner, 2009 DeGroot Prize for the best book in statistical science, awarded by the International Society for Bayesian Analysis. 2009
Description: 1 online resource (xviii, 248 pages) : illustrations.
Contents: Regression --
Classification --
Covariance functions --
Model selection and adaptation of hyperparameters --
Relationships between GPs and other models --
Theoretical perspectives --
Approximation methods for large datasets --
Appendix A : Mathematical background --
Appendix B : Guassian Markov processes.
Series Title: Adaptive computation and machine learning.
Responsibility: Carl Edward Rasmussen, Christopher K.I. Williams.

Abstract:

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

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