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  How to choose the covariance for Gaussian process regression independently of the basis

Franz, M., & Gehler, P. (2006). How to choose the covariance for Gaussian process regression independently of the basis. In Workshop Gaussian Processes in Practice (GPIP 2006) (pp. 1-4). Scottsdale, AZ, USA: videolectures.net.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D137-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-9AA2-7
Genre: Conference Paper

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GPIP-2006.pdf (Any fulltext), 61KB
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 Creators:
Franz, MO1, 2, Author              
Gehler, PV1, 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              

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 Abstract: In Gaussian process regression, both the basis functions and their prior distribution are simultaneously specified by the choice of the covariance function. In certain problems one would like to choose the covariance independently of the basis functions (e.g., in polynomial signal processing or Wiener and Volterra analysis). We propose a solution to this problem that approximates the desired covariance function at a finite set of input points for arbitrary choices of basis functions. Our experiments show that this additional degree of freedom can lead to improved regression performance.

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 Dates: 2006-06
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 4033
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Title: Workshop Gaussian Processes in Practice (GPIP 2006)
Place of Event: Bletchley Park, UK
Start-/End Date: 2006-06-12 - 2006-06-13

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Title: Workshop Gaussian Processes in Practice (GPIP 2006)
Source Genre: Proceedings
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Publ. Info: Scottsdale, AZ, USA : videolectures.net
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 4 Identifier: -