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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series),New
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A comprehensive and selfcontained 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 machinelearning community over the past decade, and this book provides a longneeded systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and selfcontained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervisedlearning 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 wellknown techniques from machine learning and statistics are discussed, including supportvector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PACBayesian 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.
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