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    Sign-methods for training with imprecise error function and gradient values

    Magoulas, George and Plagianakos, V.P. and Vrahatis, M.N. (1999) Sign-methods for training with imprecise error function and gradient values. In: UNSPECIFIED (ed.) International Joint Conference Neural Networks: IJCNN 1999. IEEE Computer Society, pp. 1768-1773. ISBN 0780355296.

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    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.


    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 06 Jul 2021 13:31
    Last Modified: 06 Jul 2021 13:31


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