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Fast generating algorithm for a general three-layer perceptron

✍ Scribed by R. Zollner; H.J. Schmitz; F. Wünsch; U. Krey

Publisher
Elsevier Science
Year
1992
Tongue
English
Weight
513 KB
Volume
5
Category
Article
ISSN
0893-6080

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✦ Synopsis

A fast iterative algorithm is proposed for the construction and the learning of a neural net achieving a classification task, with an input layer, one intermediate layer, and an output layer. The network is able to learn an arbitrary training set. The algorithm does not depend on a special learning scheme (e.g., the couplings can be determined by modified Hebbian prescriptions or by more complex learning procedures). During the process the intermediate units are constructed systematically by collecting the patterns into smaller subsets. For simplicity, we consider only the case of one output neuron, but actually this restriction is not necessar.

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