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A Class of Asymptotically Stable Algorithms for Learning-Rate Adaptation

Rüger, Stefan (1998). A Class of Asymptotically Stable Algorithms for Learning-Rate Adaptation. Algorithmica, 22(1-2), pp. 198–210.
DOI (Digital Object Identifier) Link: http://dx.doi.org/doi:10.1007/PL00013830
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Abstract

A stability criterion for learning is given. In the case of learning-rate adaptation of backpropagation, a class of asymptotically stable algorithms is presented and studied, including a convergence proof. Simulations demonstrate relevance and limitations.

Item Type: Article
ISSN: 0178-4617
Academic Unit/Department: Knowledge Media Institute
Item ID: 11955
Depositing User: Rachel Barnett
Date Deposited: 08 Oct 2008 14:13
Last Modified: 04 Apr 2011 10:23
URI: http://oro.open.ac.uk/id/eprint/11955
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