Carl Edward Rasmussen
Format: Printed Access Code
Publisher: The MIT Press (November 23, 2005)
Format: PDF / Kindle / ePub
Size: 6.3 MB
Downloadable formats: PDF
Gaussian techniques (GPs) offer a principled, sensible, probabilistic method of studying in kernel machines. GPs have got elevated recognition in the machine-learning neighborhood over the last decade, and this ebook presents a long-needed systematic and unified therapy of theoretical and useful points of GPs in computer studying. The therapy is finished and self-contained, designated at researchers and scholars in computer studying and utilized statistics.The e-book offers with the supervised-learning challenge for either regression and type, and contains specified algorithms. a large choice of covariance (kernel) capabilities are offered and their houses mentioned. version choice is mentioned either from a Bayesian and a classical point of view. Many connections to different recognized concepts from computer studying and information are mentioned, together with support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical concerns together with studying curves and the PAC-Bayesian framework are handled, and several other approximation equipment for studying with huge datasets are mentioned. The booklet includes illustrative examples and routines, and code and datasets can be found on the internet. Appendixes offer mathematical heritage and a dialogue of Gaussian Markov procedures.
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