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  Sparse Spectrum Gaussian Process Regression

Lázaro-Gredilla, M., Quiñonero-Candela, J., Rasmussen, C., & Figueiras-Vidal, A. (2010). Sparse Spectrum Gaussian Process Regression. Journal of Machine Learning Research, 11, 1865-1881.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF86-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-75C3-E
Genre: Journal Article

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Lázaro-Gredilla, M, Author
Quiñonero-Candela, J, Author              
Rasmussen, CE1, Author              
Figueiras-Vidal, AR, Author
Affiliations:
1Deparment of Engineering, University of Cambridge, ou_persistent22              

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 Abstract: We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function space representations, and note that the new construction implies priors over functions which are always stationary, and can approximate any covariance function in this class.

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 Dates: 2010-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 6664
 Degree: -

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Title: Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 1865 - 1881 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020