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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
by Carl Edward Rasmussen, Christopher K. I. Williams
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Gaussian Processes for Machine Learning unify the many diverse strands of machine learning research and to foster high quality research and innovative applications One of the most active directions in machine learning has been the development of practical Bayesian methods for challenging learning problems Gaussian Processes for Machine Learning presents one of the most important Gaussian Processes for Machine Learning Adaptive Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning series Carl Edward Rasmussen Christopher K I Williams on FREE shipping on qualifying offers A comprehensive and selfcontained introduction to Gaussian processes which provide a principled practical Gaussian Processes for Machine Learning The MIT Press 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 Gaussian Processes for Machine Learning Adaptive Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning The MIT Press Hannes Nickisch Gaussian Processes for Machine Learning GPML Toolbox The Journal of Machine Learning Research 11 p30113015 312010 Xiaoqian Jiang Bing Dong Le Xie Latanya Sweeney Adaptive Gaussian Process for ShortTerm Gaussian Processes for Machine Learning Adaptive Gaussian Processes for Machine Learning Adaptive Computation And Machine Learning Carl Edward University of Cambridge Rasmussen Christopher K I University of Edinburgh Williams ISBN 9780262182539 Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon Gaussian Processes for Machine Learning Gaussian Processes for Machine Learning Chris Williams Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh UK August 2007 Chris Williams ANC Gaussian Processes for Machine Learning Gaussian Processes for Machine Learning Gaussian Processes for Machine Learning Matthias Seeger Department of EECS University of California at Berkeley 485 Soda Hall Berkeley CA 947201776 USA mseeger February 24 2004 Abstract Gaussian processes GPs are natural generalisations of multivariate Gaussian random variables to in nite countably or continuous index sets Gaussian processes for machine learning in SearchWorks catalog Stanford Libraries official online search tool for books media journals databases government documents and more Gaussian Processes for Machine Learning Book webpage 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 CiteSeerX — Gaussian processes for machine learning Supervised learning in the form of regression for continuous outputs and classification for discrete outputs is an important constituent of statistics and machine learning either for analysis of data sets or as a subgoal of a more complex problem Traditionally parametric 1 models have been used for this purpose
