On-Line Learning in Multilayer Neural Networks
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We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.
KeywordsHide Unit Generalization Error Multilayer Neural Network Niels Bohr Institute Committee Machine
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