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Comparison of Factors for Controlling Effects in MLP Networks  

윤여창 (우석대학교 전산통계학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.5, 2004 , pp. 537-542 More about this Journal
Abstract
Multi-Layer Perceptron network has been mainly applied to many practical problems because of its nonlinear mapping ability. However the generalization ability of MLP networks may be affected by the number of hidden nodes, the initial values of weights and the training errors. These factors, if improperly chosen, may result in poor generalization ability of MLP networks. It is important to identify these factors and their interaction in order to control effectively the generalization ability of MLP networks. In this paper, we have empirically identified the factors that affect the generalization ability of MLP networks, and compared their relative effects on the generalization performance for the conventional and visualized weight selecting methods using the controller box.
Keywords
Multi-Layer Perceptron; controller box; complexity control;
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Times Cited By KSCI : 1  (Citation Analysis)
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