Gaussian Processes for Machine Learning
Gaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams
eBook Details :
Hardcover: 266 pages
Publisher:The MIT Press 2005
Language: English
ISBN :13: 9780262182539
License(s): This book is © Copyright 2006 by Massachusetts Institute of Technology. The MIT Press have kindly agreed to allow us to make the book available on the web. The web version of the book corresponds to the 2nd printing.
eBook Description:
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. 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.
