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  Approximation Methods for Gaussian Process Regression

Quiñonero-Candela, J., Rasmussen, C., & Williams, C. (2007). Approximation Methods for Gaussian Process Regression. In Large-Scale Kernel Machines (pp. 203-223). Cambridge, MA, USA: MIT Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBFB-A Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBFC-8
Genre: Book Chapter

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Quiñonero-Candela, J1, Author           
Rasmussen, CE1, Author           
Williams, CKI, Author
Bottou, Editor
L., Editor
Chapelle, O., Editor
DeCoste, D., Editor
Weston, J., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://mitpress.mit.edu/9780262026253
BibTex Citekey: 4798
 Degree: -

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Title: Large-Scale Kernel Machines
Source Genre: Book
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 203 - 223 Identifier: -