English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Gaussian Processes for Machine Learning

Rasmussen, C., & Williams, C. (2006). Gaussian Processes for Machine Learning. Cambridge, MA, USA: MIT Press.

Item is

Files

show Files

Locators

show
hide
Locator:
https://ieeexplore.ieee.org/book/6267323 (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Rasmussen, CE1, 2, Author           
Williams, CKI, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2006
 Publication Status: Published in print
 Pages: 248
 Publishing info: Cambridge, MA, USA : MIT Press
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3569
ISBN: 978-0-262-18253-9
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Adaptive Computation and Machine Learning
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -