The learning dynamics of a universal approximator


The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Publication ISSN: 1049-5258
Last Modified: 29 Nov 2023 09:59
Date Deposited: 11 Mar 2019 17:28
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Related URLs: http://mitpress ... type=2&tid=3990 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 1997-05
Authors: West, Ansgar H. L.
Saad, David
Nabney, Ian T.



Version: Published Version

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