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Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams.

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learningPublisher: Cambridge, Mass. : MIT Press, [2006]Copyright date: ©2006Description: xviii, 248 pages : illustrations ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 026218253X
  • 9780262182539
Subject(s): DDC classification:
  • 519.23 22
LOC classification:
  • QA274.4 .R37 2006
Contents:
1. Introduction -- 2. Regression -- 3. Classification -- 4. Covariance functions -- 5. Model selection and adaptation of hyperparameters -- 6. Relationships between GPs and other models -- 7. Theoretical perspectives -- 8. Approximation methods for large datasets -- 9. Further issues and conclusions.
Review: "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."--BOOK JACKET.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Book City Campus City Campus Main Collection 519.23 RAS (Browse shelf(Opens below)) 1 Available A402039B

Includes bibliographical references (pages 223-238) and index.

1. Introduction -- 2. Regression -- 3. Classification -- 4. Covariance functions -- 5. Model selection and adaptation of hyperparameters -- 6. Relationships between GPs and other models -- 7. Theoretical perspectives -- 8. Approximation methods for large datasets -- 9. Further issues and conclusions.

"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."--BOOK JACKET.

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