Globally optimal on-line learning rules


We present a method for determining the globally optimal on-line learning rule for a soft committee machine under a statistical mechanics framework. This work complements previous results on locally optimal rules, where only the rate of change in generalization error was considered. We maximize the total reduction in generalization error over the whole learning process and show how the resulting rule can significantly outperform the locally optimal rule.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Publication ISSN: 1049-5258
Last Modified: 29 Nov 2023 10:00
Date Deposited: 11 Mar 2019 17:29
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://mitpress ... type=2&tid=8363 (Publisher URL)
PURE Output Type: Article
Published Date: 1998-01
Authors: Rattray, Magnus
Saad, David



Version: Published Version

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