Learning in Single Hidden Layer Feedforward Network Models: Backpropagation in a Real World Application

    Publication: Working/Discussion PaperWU Working Paper

    Abstract

    Leaming in neural networks has attracted considerable interest in recent years. Our focus is
    on learning in single hidden layer feedforward networks which is posed as a search in the
    network parameter space for a network that minimizes an additive error function of
    statistically independent examples. In this contribution, we review first the class of single
    hidden layer feedforward networks and characterize the learning process in such networks
    from a statistical point of view. Then we describe the backpropagation procedure, the leading
    case of gradient descent learning algorithms for the class of networks considered here, as
    well as an efficient heuristic modification. Finally, we analyse the applicability of these
    learning methods to the problem of predicting interregional telecommunication flows.
    Particular emphasis is laid on the engineering judgment, first, in choosing appropriate
    values for the tunable parameters, second, on the decision whether to train the network by
    epoch or by pattern (random approximation), and, third, on the overfitting problem. In
    addition, the analysis shows that the neural network model whether using either epoch-based
    or pattern-based stochastic approximation outperforms the classical regression approach to
    modelling telecommunication flows. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    Publication statusPublished - 1 Apr 1994

    Publication series

    NameDiscussion Papers of the Institute for Economic Geography and GIScience
    No.39/94

    WU Working Paper Series

    • Discussion Papers of the Institute for Economic Geography and GIScience

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