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Truncated covariance matrices and Toeplitz methods in Gaussian processes

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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=819541
Original languageEnglish
Title of host publicationArtificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) (Volume:1 )
Place of PublicationEDISON
PublisherINST ELECTRICAL ENGINEERS INSPEC INC
Pages55-60
Number of pages6
ISBN (Print)0-85296-721-7
DOIs
Publication statusPublished - 1999
Event9th International Conference on Artificial Neural Networks (ICANN99) - EDINBURGH, United Kingdom
Duration: 7 Sep 199910 Sep 1999

Publication series

NameIEE CONFERENCE PUBLICATIONS
PublisherINST ELECTRICAL ENGINEERS INSPEC INC
ISSN (Print)0537-9989

Conference

Conference9th International Conference on Artificial Neural Networks (ICANN99)
CountryUnited Kingdom
Period7/09/9910/09/99

Abstract

Gaussian processes are a limit extension of neural networks. Standard Gaussian process techniques use a squared exponential covariance function. Here, the use of truncated covariances is proposed. Such covariances have compact support. Their use speeds up matrix inversion and increases precision. Furthermore they allow the use of speedy, memory efficient Toeplitz inversion for high dimensional grid based Gaussian process predictors.

Event

9th International Conference on Artificial Neural Networks (ICANN99)

7/09/9910/09/99

United Kingdom

Event: Conference

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