Globally optimal parameters for on-line learning in multilayer neural networks

David Saad, Magnus Rattray

Research output: Contribution to journalArticle

Abstract

We present a framework for calculating globally optimal parameters, within a given time frame, for on-line learning in multilayer neural networks. We demonstrate the capability of this method by computing optimal learning rates in typical learning scenarios. A similar treatment allows one to determine the relevance of related training algorithms based on modifications to the basic gradient descent rule as well as to compare different training methods.
Original languageEnglish
Pages (from-to)2578-2581
Number of pages4
JournalPhysical Review Letters
Volume79
Issue number13
DOIs
Publication statusPublished - 29 Sep 1997

Bibliographical note

Copyright of the American Physical Society

Keywords

  • on-line learning
  • multilayer neural networks
  • learning rates
  • training algorithms

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