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Gaussian Processes for Machine Learning

Titre :

Gaussian Processes for Machine Learning

Caractéristiques :


Auteur(s) :RASMUSSEN
Editeur :M.I.T. PRESS
Parution :01/2006
Langue :Anglais Anglais
Nbre de pages :272
ISBN :978-0-262-18253-9
Reliure :Hardcover
Prix :42.00 € ttc
Disponibilité :Livraison sous 10 jours ouvrés.

Couverture :


Gaussian Processes for Machine Learning

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.

Carl Edward Rasmussen is a Research Scientist at the Department of
Empirical Inference for Machine Learning and Perception at the Max
Planck Institute for Biological Cybernetics, Tübingen.

Christopher K. I. Williams is Professor of Machine Learning and Director
of the Institute for Adaptive and Neural Computation in the School of
Informatics, University of Edinburgh.


Table of contents :

Series Foreword
Preface
Symbols and Notation xvii
1 Introduction 1
2 Regression 7
3 Classification 33
4 Covariance Functions 79
5 Model Selection and Adaptation of Hyperparameters 105
6 Relationships between GPs and Other Models 129
7 Theoretical Perspectives 151
8 Approximation Methods for Large Datasets 171
9 Further Issues and Conclusions 189
Appendix A Mathematical Background 199
Appendix B Gaussian Markov Process 207
Appendix C Datasets and Code 221
Bibliography
Author Index 239
Subject Index 245

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