Effective neural network training with a different learning rate for each weight

Magoulas, George and Plagianakos, V.P. and Vrahatis, M.N. (1999) Effective neural network training with a different learning rate for each weight. In: UNSPECIFIED (ed.) 6th IEEE International Conference on Electronics, Circuits and Systems. IEEE Computer Society, pp. 591-594. ISBN 0780356829.

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Official URL: https://doi.org/10.1109/ICECS.1999.812354

Abstract

Batch training algorithms with a different learning rate for each weight are investigated. The adaptive learning rate algorithms of this class that apply inexact one-dimensional subminimization are analyzed and their global convergence is studied. Simulations are conducted to evaluate the convergence behavior of two training algorithms of this class and to compare them with several popular training methods.

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 12:32
Last Modified:	09 Aug 2023 12:51
URI:	https://eprints.bbk.ac.uk/id/eprint/45005

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