On-line learning in multilayer neural networks

David Saad, Sara A. Solla

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.
Original languageEnglish
Title of host publicationProceedings of the first international conference on mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Place of PublicationOxford
PublisherKluwer
ISBN (Print)0-7923-99331
DOIs
Publication statusPublished - 1997

Bibliographical note

© Springer Science+Business Media New York 1997

Keywords

  • algorithm
  • design
  • measurement
  • performance
  • theory
  • verification

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