Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning series Carl Edward Rasmussen Christopher K I Williams 9780262182539 Books Télécharger SCJ

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Lecteur PDF gratuit Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning series Carl Edward Rasmussen Christopher K I Williams 9780262182539 Books KEV

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A 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.

Carl Edward Rasmussen, Christopher K. I. Williams,Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series),The MIT Press,026218253X,9780262182539,Machine Theory,Gaussian processes - Data processing,Gaussian processes;Data processing.,Machine learning - Mathematical models,Machine learning;Mathematical models.,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computer Applications,Computer Books General,Computer Science/Machine Learning Neural Networks,Computer science,Computer/General,Computers,Computers - General Information,Computers/Machine Theory,Data processing,Gaussian processes,Intelligence (AI) Semantics,Machine learning,Mathematical models,Non-Fiction,Psychology,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States

Gaussian Processes for Machine Learning Adaptive Computation and Machine Learning series Carl Edward Rasmussen Christopher K I Williams 9780262182539 Books Reviews :

A 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.

Carl Edward Rasmussen, Christopher K. I. Williams,Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series),The MIT Press,026218253X,9780262182539,Machine Theory,Gaussian processes - Data processing,Gaussian processes;Data processing.,Machine learning - Mathematical models,Machine learning;Mathematical models.,COMPUTERS / Computer Science,COMPUTERS / Intelligence (AI) Semantics,COMPUTERS / Machine Theory,Computer Applications,Computer Books General,Computer Science/Machine Learning Neural Networks,Computer science,Computer/General,Computers,Computers - General Information,Computers/Machine Theory,Data processing,Gaussian processes,Intelligence (AI) Semantics,Machine learning,Mathematical models,Non-Fiction,Psychology,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) [Carl Edward Rasmussen, Christopher K. I. Williams] on . PBA comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical

 

Product details Series Adaptive Computation and Machine Learning series Hardcover 272 pages Publisher The MIT Press (November 23, 2005) Language English ISBN-10 9780262182539 ISBN-13 978-0262182539 ASIN 026218253X

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