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:	https://doi.org/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:	Journal Item
Copyright Holders:	1998 Springer-Verlag
ISSN:	1432-0541
Keywords:	convex optimization; neural learning; learning-rate adaptation; line search; stability criterion
Academic Unit/School:	Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID:	11955
Depositing User:	Users 8580 not found.
Date Deposited:	08 Oct 2008 13:13
Last Modified:	02 May 2018 12:53
URI:	http://oro.open.ac.uk/id/eprint/11955
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