Gaussian processes for machine learning
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Author
Contributions
- Williams, Christopher K. I. - Contributor
Publication
2006 - MIT Press, Cambridge, Mass, Massachusetts
Language
English
Word Count
68,000 words, Guess
Page Count
272 pages
Identifiers
- Internet Archivegaussianprocesse00rasm_125
- Internet Archivegaussianprocesse00rasm_843
- Internet Archivegaussianprocesse00carl
- Internet Archivegaussianprocesse00carl_651
- ISBN-139780262182539
and 6 more
- ISBN-10026218253X
- WikidataQ56662583
- Library of Congress Control Number2005053433
- OCLC Control Number61285753
- Better World Books9780262182539
- Open LibraryOL3428758M
Classifications
- DDC519.2/3
- LCCQA274.4 .R37 2006
- LCCQA274.4.R37 2006
Description
Gaussian processes (GPs) provide an approach to kernel-machine learning. This book provides a 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. (From the book's web site, http://www.gaussianprocess.org/gpml/ )
Subjects
Topics
Series Statement
- Adaptive computation and machine learning
Other Editions
- Gaussian processes for machine learning
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