Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams

Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams
Publisher: The MIT Press 2005
ISBN/ASIN: 026218253X
ISBN-13: 9780262182539
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.
Computers & Internet Computer Science Artificial Intelligence Machine Learning

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Gaussian Processes for Machine Learning by Carl E. Rasmussen, Christopher K. I. Williams