Dynamics of on-line gradient descent learning for multilayer neural networks

Abstract

We consider the problem of on-line gradient descent learning for general two-layer neural networks. An analytic solution is presented and used to investigate the role of the learning rate in controlling the evolution and convergence of the learning process.

Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of Massachusetts Institute of Technology Press (MIT Press) http://mitpress.mit.edu/mitpress/copyright/default.asp
Uncontrolled Keywords: on-line,gradient descent learning,general two-layer neural networks,learning rate,learning process.
Publication ISSN: 1049-5258
Full Text Link:
Related URLs: http://mitpress ... type=2&tid=8421 (Publisher URL)
PURE Output Type: Article
Published Date: 1996
Authors: Saad, David (ORCID Profile 0000-0001-9821-2623)
Solla, Sara A.

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