Globally optimal on-line learning rules

Magnus Rattray; David Saad; Michael I. Jordan; Michael J. Kearns; Sara A. Solla

Globally optimal on-line learning rules

Magnus Rattray, David Saad, Michael I. Jordan (Editor), Michael J. Kearns (Editor), Sara A. Solla (Editor)

Mathematics

Research output: Contribution to journal › Article › peer-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 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.

Original language	English
Pages (from-to)	322-328
Number of pages	7
Journal	Advances in Neural Information Processing Systems
Volume	10
Publication status	Published - Jan 1998
Event	Advances in Neural Information Processing Systems 1994 - Singapore, Singapore Duration: 16 Nov 1994 → 18 Nov 1994

Bibliographical note

Copyright of the Massachusetts Institute of Technology Press (MIT Press)

Keywords

on-line learning
statistical mechanics
generalization error
optimal rule
resulting rule

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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.",

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author = "Magnus Rattray and David Saad and Jordan, {Michael I.} and Kearns, {Michael J.} and Solla, {Sara A.}",

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A2 - Kearns, Michael J.

A2 - Solla, Sara A.

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AB - 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.

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KW - statistical mechanics

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KW - optimal rule

KW - resulting rule

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UR - http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8363

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T2 - Advances in Neural Information Processing Systems 1994

Y2 - 16 November 1994 through 18 November 1994

ER -

Globally optimal on-line learning rules

Abstract

Bibliographical note

Keywords

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