Globally optimal on-line learning rules for multi-layer neural networks

Magnus Rattray*, David Saad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This rule maximizes the total reduction in generalization error over the whole learning process. A simple example demonstrates that the locally optimal rule, which maximizes the rate of decrease in generalization error, may perform poorly in comparison.

Original languageEnglish
Pages (from-to)L771-L776
JournalJournal of Physics A: Mathematical and General
Volume30
Issue number22
DOIs
Publication statusPublished - 21 Nov 1997

Bibliographical note

Copyright of the Institute of Physics.

Keywords

  • globally optimal on-line learning
  • soft committee machine
  • error
  • locally optimal rule

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