Gaussian Processes for Machine Learning
Carl Edward Rasmussen, University of Cambridge;
Christopher K. I. Williams, University of Edinburgh
The MIT Press, 2006
ISBN: 978-0-262-18253-9;
Language: English
Gaussian Processes for Machine Learning provides a principled, practical, probabilistic approach to learning using kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book focuses on 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 classical perspective.
