Rattray, Magnus and Saad, David (1997). Globally optimal on-line learning rules for multi-layer neural networks. Journal of Physics A: Mathematical and General, 30 (22), L771-L776.
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.
Publication DOI: | https://doi.org/10.1088/0305-4470/30/22/005 |
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Divisions: | College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
Additional Information: | Copyright of the Institute of Physics. |
Uncontrolled Keywords: | globally optimal on-line learning,soft committee machine,error,locally optimal rule,General Physics and Astronomy,Statistical and Nonlinear Physics,Mathematical Physics |
Publication ISSN: | 0305-4470 |
Last Modified: | 06 Aug 2024 07:02 |
Date Deposited: | 11 Mar 2019 17:28 |
Full Text Link: | |
Related URLs: |
http://www.scop ... tnerID=8YFLogxK
(Scopus URL) http://iopscien ... ect=.iopscience (Publisher URL) |
PURE Output Type: | Article |
Published Date: | 1997-11-21 |
Authors: |
Rattray, Magnus
Saad, David ( ![]() |