Gaussian Processes for Machine Learning
by Carl E. Rasmussen, Christopher K. I. Williams
Publisher: The MIT Press 2005
Number of pages: 266
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.
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