Online approximations for wind-field models

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Abstract

We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data. Our approach combines a sequential update of a Gaussian approximation to the posterior with a sparse representation that allows to treat problems with a large number of observations.

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Details

Publication date1 Jan 2001
Publication titleArtificial Neural Networks — ICANN 2001
PublisherSpringer
Pages300-307
Number of pages8
Volume2130
ISBN (Print)9783540424864
Original languageEnglish
EventInternational Conference on Neural Networks -

Publication series

NameLecture Notes in Computer Science
PublisherSpringer-Verlag

Conference

ConferenceInternational Conference on Neural Networks
Period1/01/011/01/01

Bibliographic note

The original publication is available at www.springerlink.com

    Keywords

  • online approximations, Gaussian process models, spatially distributed systems, scatterometer data, Gaussian approximation

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