Globally convergent algorithms with local learning rates

Magoulas, George and Plagianakos, V.P. and Vrahatis, M.N. (2002) Globally convergent algorithms with local learning rates. IEEE Transactions on Neural Networks 13 (3), pp. 774-779. ISSN 1045-9227.

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Official URL: https://doi.org/10.1109/TNN.2002.1000148

Abstract

A novel generalized theoretical result is presented that underpins the development of globally convergent first-order batch training algorithms which employ local learning rates. This result allows us to equip algorithms of this class with a strategy for adapting the overall direction of search to a descent one. In this way, a decrease of the batch-error measure at each training iteration is ensured, and convergence of the sequence of weight iterates to a local minimizer of the batch error function is obtained from remote initial weights. The effectiveness of the theoretical result is illustrated in three application examples by comparing two well-known training algorithms with local learning rates to their globally convergent modifications.

Metadata

Item Type:	Article
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	29 Jun 2021 13:52
Last Modified:	09 Aug 2023 12:51
URI:	https://eprints.bbk.ac.uk/id/eprint/44919

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