On-line learning with adaptive back-propagation in two-layer networks


An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, we analyse these learning algorithms in both the symmetric and the convergence phase for finite learning rates in the case of uncorrelated teachers of similar but arbitrary length T. These analyses show that adaptive back-propagation results generally in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the American Physical Society
Publication ISSN: 1550-2376
Last Modified: 29 Nov 2023 10:00
Date Deposited: 11 Mar 2019 17:28
Full Text Link: 10.1103/PhysRevE.56.3426
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://prola.ap ... /v56/i3/p3426_1 (Publisher URL)
PURE Output Type: Article
Published Date: 1997-09
Authors: West, Ansgar H.L.
Saad, David



Version: Accepted Version

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