Deterministic nonmonotone strategies for effective training of multilayer perceptrons

Plagianakos, V.P. and Magoulas, George and Vrahatis, M.N. (2002) Deterministic nonmonotone strategies for effective training of multilayer perceptrons. IEEE Transactions on Neural Networks 13 (6), pp. 1268-1284. ISSN 1045-9227.

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Official URL: https://doi.org/10.1109/TNN.2002.804225

Abstract

We present deterministic nonmonotone learning strategies for multilayer perceptrons (MLPs), i.e., deterministic training algorithms in which error function values are allowed to increase at some epochs. To this end, we argue that the current error function value must satisfy a nonmonotone criterion with respect to the maximum error function value of the M previous epochs, and we propose a subprocedure to dynamically compute M. The nonmonotone strategy can be incorporated in any batch training algorithm and provides fast, stable, and reliable learning. Experimental results in different classes of problems show that this approach improves the convergence speed and success percentage of first-order training algorithms and alleviates the need for fine-tuning problem-depended heuristic parameters.

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/44920

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