Development and convergence analysis of training algorithms with local learning rate adaptation

Magoulas, George and Plagianakos, V.P. and Vrahatis, M.N. (2000) Development and convergence analysis of training algorithms with local learning rate adaptation. In: UNSPECIFIED (ed.) Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IEEE Computer Society, pp. 21-26. ISBN 0769506194.

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Official URL: https://doi.org/10.1109/IJCNN.2000.857808

Abstract

A new theorem for the development and convergence analysis of supervised training algorithms with an adaptive learning rate for each weight is presented. Based on this theoretical result, a strategy is proposed to automatically adapt the search direction, as well as the step-size length along the resultant search direction. This strategy is applied to some well known local learning algorithms to investigate its effectiveness.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	06 Jul 2021 11:07
Last Modified:	09 Aug 2023 12:51
URI:	https://eprints.bbk.ac.uk/id/eprint/44996

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