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Gaussian Processes for Machine Learning

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Gaussian Processes for Machine Learning Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

About the Author

Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University of Cambridge, and Adjunct Research Scientist 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.

Product Details

ISBN:
9780262182539
Author:
Rasmussen, Carl Edward
Publisher:
Mit Press
Author:
Williams, Christopher K. I.
Location:
Cambridge
Subject:
Mathematical models
Subject:
Machine Theory
Subject:
Machine learning
Subject:
Artificial Intelligence - General
Subject:
Computer Science
Subject:
Intelligence (AI) & Semantics
Subject:
Gaussian processes - Data processing
Subject:
Machine learning - Mathematical models
Subject:
Computers-Reference - General
Copyright:
Series:
Adaptive Computation and Machine Learning series Gaussian Processes for Machine Learning
Publication Date:
20051131
Binding:
HARDCOVER
Grade Level:
from 17
Language:
English
Pages:
266
Dimensions:
10 x 8 in

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
Religion » Comparative Religion » General

Gaussian Processes for Machine Learning New Hardcover
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Product details 266 pages MIT Press - English 9780262182539 Reviews:
"Synopsis" by , A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
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