Globally optimal on-line learning rules

Rattray, Magnus and Saad, David (1998). Globally optimal on-line learning rules. Advances in Neural Information Processing Systems, 10 , pp. 322-328.

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 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: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Uncontrolled Keywords: on-line learning,statistical mechanics,generalization error,optimal rule,resulting rule
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://mitpress ... type=2&tid=8363 (Publisher URL)
Published Date: 1998-01
Authors: Rattray, Magnus
Saad, David ( 0000-0001-9821-2623)

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