Online approximations for wind-field models

Lehel Csató, Dan Cornford, Manfred Opper

Research output: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks — ICANN 2001
PublisherSpringer
Pages300-307
Number of pages8
Volume2130
ISBN (Print)9783540424864
DOIs
Publication statusPublished - 1 Jan 2001
EventInternational Conference on Neural Networks -
Duration: 1 Jan 20011 Jan 2001

Publication series

NameLecture Notes in Computer Science
PublisherSpringer-Verlag

Conference

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

Bibliographical note

The original publication is available at www.springerlink.com

Keywords

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

Cite this

Csató, L., Cornford, D., & Opper, M. (2001). Online approximations for wind-field models. In Artificial Neural Networks — ICANN 2001 (Vol. 2130, pp. 300-307). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/3-540-44668-0_43