BIROn - Birkbeck Institutional Research Online

    Nonmonotone methods for backpropagation training with adaptive learning rate

    Palgianakos, V.P. and Vrahatis, M.N. and Magoulas, George (1999) Nonmonotone methods for backpropagation training with adaptive learning rate. In: UNSPECIFIED (ed.) International Joint Conference Neural Networks: IJCNN 1999. IEEE Computer Society, pp. 1762-1767. ISBN 0780355296.

    Full text not available from this repository.

    Abstract

    We present nonmonotone methods for feedforward neural network training, i.e., training methods in which error function values are allowed to increase at some iterations. More specifically, at each epoch we impose that the current error function value must satisfy an Armijo-type criterion, with respect to the maximum error function value of M previous epochs. A strategy to dynamically adapt M is suggested and two training algorithms with adaptive learning rates that successfully employ the above mentioned acceptability criterion are proposed. Experimental results show that the nonmonotone learning strategy improves the convergence speed and the success rate of the methods considered.

    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 13:27
    Last Modified: 06 Jul 2021 13:27
    URI: https://eprints.bbk.ac.uk/id/eprint/45008

    Statistics

    Downloads
    Activity Overview
    0Downloads
    9Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item