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Gaussian Processes for Machine Learning

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Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning

270 pages, parution le 25/04/2006

Résumé

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.

L'auteur Carl Edward Rasmussen

Carl Edward Rasmussen is a Research Scientist at the Department of Empirical Inference for Machine Learning and Perception at the Max Planck Institute for Biological Cybernetics, Tübingen.

L'auteur Christopher K. I. Williams

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.

Sommaire

  • Introduction
  • Regression
  • Classification
  • Covariance functions
  • Model selection and adaptation of hyperparameters
  • Relationships between GPs and other models
  • Theoretical perspectives
  • Approximation methods for large datasets
  • Further issues and conclusions
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Caractéristiques techniques du livre "Gaussian Processes for Machine Learning"

  PAPIER
Éditeur(s) The MIT Press
Auteur(s) Carl Edward Rasmussen, Christopher K. I. Williams
Parution 25/04/2006
Nb. de pages 270
Format 21 x 26
Couverture Broché
Poids 740g
Intérieur Noir et Blanc
EAN13 9780262182539
ISBN13 978-0-262-18253-9
Sélection de Noël

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