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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 L. Bottou, O. Chapelle, D. DeCoste, & J. Weston (Eds.), 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/21.11116/0000-0003-E8EE-C
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Quiñonero-Candela, J, Author           
Rasmussen, CE1, 2, Author           
Williams, CKI, Author
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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              

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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: 2007-08
 Publication Status: Issued
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 Identifiers: BibTex Citekey: 4798
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Title: Large-Scale Kernel Machines
Source Genre: Book
 Creator(s):
Bottou, L, Editor
Chapelle, O1, Editor           
DeCoste, D, Editor
Weston, J, Editor           
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 203 - 223 Identifier: ISBN: 0-262-02625-2

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Title: Neural information processing series
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