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    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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    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: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jul 2021 11:07
    Last Modified: 06 Jul 2021 11:07
    URI: https://eprints.bbk.ac.uk/id/eprint/44996

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