Gaussian processes for machine learning
Title:
Gaussian processes for machine learning
JLCTITLE245:
Carl Edward Rasmussen, Christopher K.I. Williams.
Personal Author:
Publication Information:
Cambridge, Mass. : MIT Press, c2006.
Physical Description:
xviii, 248 p. : ill. ; 26 cm.
ISBN:
9780262182539
Series Title:
Adaptive computation and machine learning
Abstract:
"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.
Bibliography Note:
Includes bibliographical references (p. [223]-238) and indexes.
Added Author:
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.