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Gaussian Processes Machine Learning by Carl Edward (33 results)
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Gaussian Processes for Machine Learning
Language: English
Published by The MIT Press Bookstore, 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Condition: New. pp. xviii + 248.
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Condition: New.
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Condition: New. Brand New. Excellent Customer Service.
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Hardcover. Condition: New.
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Condition: As New. Unread book in perfect condition.
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) Format: Hardcover
Language: English
Published by Penguin Random House, 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Condition: As New. Unread copy in mint condition.
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) Format: Hardcover
Language: English
Published by Penguin Random House, 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Condition: New. Brand New.
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Gaussian Processes for Machine Learning (Hardcover)
Language: English
Published by MIT Press Ltd, Cambridge, Mass., 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Hardcover. Condition: new. Hardcover. 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. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Gaussian Processes for Machine Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 4 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Hardback. Condition: New.
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Gaussian Processes for Machine Learning
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New.
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Gaussian Processes for Machine Learning
Language: English
Published by The MIT Press Bookstore, 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Condition: New. pp. xviii + 248 Illus.
-
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
-
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
-
hardcover. Condition: New.
-
Gaussian Processes for Machine Learning
Language: English
Published by The MIT Press Bookstore, 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Condition: New. pp. xviii + 248.
-
Gaussian Processes for Machine Learning
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New. 2006. Hardcover. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Series: Adaptive Computation and Machine Learning Series. Num Pages: 266 pages, Illustrations. BIC Classification: PBW; UYQM. Category: (P) Professional & Vocational. Dimension: 261 x 212 x 18. Weight in Grams: 720. . . . . .
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Gaussian Processes for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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hardcover. Condition: New.
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Hardcover. Condition: Brand New. 2448 pages. 10.00x7.00x1.00 inches. In Stock.
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Gaussian Processes for Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: New. 2006. Hardcover. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Series: Adaptive Computation and Machine Learning Series. Num Pages: 266 pages, Illustrations. BIC Classification: PBW; UYQM. Category: (P) Professional & Vocational. Dimension: 261 x 212 x 18. Weight in Grams: 720. . . . . . Books ship from the US and Ireland.
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Gaussian Processes for Machine Learning
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. New copy - Usually dispatched within 4 working days.
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Hardcover. Condition: Good. No Jacket. 1st Edition. Gaussian Processes for Machine Learning, by Carl Edward Rasmussen and Christopher KL Williams, published by The MIT Press. Firsts edition,2006. Hardback with dark blue paper. No dust wrapper. Embossed silver titles to spine. the cover is in good condition with a a couple of scuffs and faint water marks. Slight bumping to top and bottom of spine. Contents are in fine condition with the exception of two tiny brown marks on the outer page edges. 219 pages plus appendix, bibliography and index.
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Hardback. Condition: New.
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Gaussian Processes for Machine Learning (Hardcover)
Language: English
Published by MIT Press Ltd, Cambridge, Mass., 2005
ISBN 10: 026218253X ISBN 13: 9780262182539
Hardcover. Condition: new. Hardcover. 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. A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
