Gaussian processes for machine learning

A specific advantage of this book is that it is one of the few that devotes an entire chapter to the connection between Bayesian methods using Gaussian processes and the reproduction of the Hilbert spaces of the Kernel. While this connection is pretty obvious in hindsight, it’s nice that it’s clearly broken down into small, understandable parts. Otherwise, all the explanations relating to the Gaussian processes for regression and classification are very clear and make this book an interesting read. I would also recommend reading other books that focus more on reproducing the Hilbert spaces of the kernel to get a full picture of these methods (eg “Learning with Kernels” by Scholkopf and Smola or for an even bigger picture “Generalized additive models” by Hastie and Tibshirani). Finally, as GP and RKHS for classification are still evolving topics, it is probably a good idea to continue reading more material about them after finishing this book.

Gaussian processes for machine learning PDF

Book Description:

Gaussian processes (GPs) provide a principled, practical, and probabilistic approach to learning in kernel machines. General practitioners 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 the theoretical and practical aspects of general practitioners in machine learning. The treatment is complete and autonomous, aimed at researchers and students of machine learning and applied statistics. The book addresses the problem of supervised learning 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 from both a Bayesian and a classical perspective. Many connections to other well-known techniques of machine learning and statistics are discussed, including support vector machines, neural networks, splines, regularization networks, relevance vector machines, and others. Theoretical issues are covered, including learning curves and the PAC-Bayesian framework, and various approaches to learning with large datasets are discussed. The book contains illustrative examples and exercises, and the code and data sets are available on the Web. The appendices provide a mathematical background and a discussion of the Gaussian Markov processes.

Gaussian processes for machine learning

Author(s): Williams, Christopher K. I.;Rasmussen, Carl Edward

Series: Adaptive computation and machine learning

Publisher: MIT Press, Year: 2008

ISBN: 026218253X,9780262182539


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