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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) Hardback - 2005

by Williams, Christopher K. I

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Details

  • Title Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
  • Author Williams, Christopher K. I
  • Binding Hardback
  • Edition First Edition Re
  • Condition Used - Very good
  • Pages 272
  • Volumes 1
  • Language ENG
  • Publisher MIT Press
  • Publication date 2005-11
  • Features Bibliography, Index
  • Bookseller's Inventory # GOR008121298
  • ISBN 9780262182539 / 026218253X
  • Weight 1.65 lbs (0.75 kg)
  • Dimensions 10.22 x 8.26 x 0.73 in (25.96 x 20.98 x 1.85 cm)
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Computers - General Information
  • Library of Congress subjects Gaussian processes - Data processing, Machine learning - Mathematical models
  • Library of Congress Catalogue Number 2005053433
  • Dewey Decimal Code 519.23
  • Quantity available 1

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From the publisher

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.

About the author

Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University of Cambridge, and Adjunct Research Scientist at the Max Planck Institute for Biological Cybernetics, Tbingen.

Christopher K. I. Williams is Professor of Machine Learning and Director of the Institute for Adaptive and Neural Computation in the School of Informatics, University of Edinburgh.

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