Titre :	Gaussian Processes for Machine Learning
Type de document :	texte imprimé
Auteurs :	Carl Edward Rasmussen (1969-....), Auteur ; Christopher K. I. Williams, Auteur
Editeur :	Cambridge, Mass. : MIT Press
Année de publication :	2006, cop. 2006
Collection :	Adaptive computation and machine learning
Importance :	1 vol. (XVIII-248 p.)
Présentation :	graphiques, figures, illustrations, jaquette illustrée
Format :	26 cm
ISBN/ISSN/EAN :	978-0-262-18253-9
Note générale :	PPN 097588938 .- ISBN 0-262-18253-X (rel.) Document accessible en ligne sur Mit Press direct (https://direct.mit.edu/books/oa-monograph/2320/Gaussian-Processes-for-Machine-Learning ; https://gaussianprocess.org/gpml/chapters/RW.pdf)
Langues :	Anglais (eng)
Tags :	Processus gaussiens -- Informatique  Apprentissage automatique -- Modèles mathématiques  Markov, Processus de  Gaussian processes -- Data processing  Machine learning -- Mathematical models  Markov processes
Index. décimale :	519.23 Processus probabilistes - Processus stochastiques - Processus gaussiens
Résumé :	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.
Note de contenu :	Bibliographie p. [223]-238. Index auteurs p.[239]-243. Index sujet p.[244]-248
En ligne :	https://direct.mit.edu/books/oa-monograph/2320/Gaussian-Processes-for-Machine-Le [...]

Gaussian Processes for Machine Learning [texte imprimé] / Carl Edward Rasmussen (1969-....), Auteur ; Christopher K. I. Williams, Auteur . - Cambridge, Mass. : MIT Press, 2006, cop. 2006 . - 1 vol. (XVIII-248 p.) : graphiques, figures, illustrations, jaquette illustrée ; 26 cm. - (Adaptive computation and machine learning) .
ISBN : 978-0-262-18253-9
PPN 097588938 .- ISBN 0-262-18253-X (rel.) Document accessible en ligne sur Mit Press direct (https://direct.mit.edu/books/oa-monograph/2320/Gaussian-Processes-for-Machine-Learning ; https://gaussianprocess.org/gpml/chapters/RW.pdf)
Langues : Anglais (eng)

Tags :	Processus gaussiens -- Informatique  Apprentissage automatique -- Modèles mathématiques  Markov, Processus de  Gaussian processes -- Data processing  Machine learning -- Mathematical models  Markov processes
Index. décimale :	519.23 Processus probabilistes - Processus stochastiques - Processus gaussiens
Résumé :	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.
Note de contenu :	Bibliographie p. [223]-238. Index auteurs p.[239]-243. Index sujet p.[244]-248
En ligne :	https://direct.mit.edu/books/oa-monograph/2320/Gaussian-Processes-for-Machine-Le [...]