000			02109nam a22001817a 4500
008			211013b2006 |||||||| |||| 00| 0 eng d
020			_a9780262182539
082			_a519.21 RAS-C
100			_aRasmussen, Carl Edward
245			_aGaussian processes for machine learning / _cCarl Edward Rasmussen and Christopher K. I. Williams
260			_aLondon _bThe MIT Press _c2006
300			_a248 p.
365			_aUSD _b50.00.
500			_aA comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 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. 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. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
650			_aMachine learning--Mathematical models
650			_aGaussian processes--Data processing
700			_aWilliams, Christopher K. I.
999			_c68759 _d68759