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Learning in feedforward layered networks: the tiling algorithm

Journal of Physics A Mathematical and General · 1989 · Vol. 22(12) · pp. 2191–2203

Abstract

The authors propose a new algorithm which builds a feedforward layered network in order to learn any Boolean function of N Boolean units. The number of layers and the number of hidden units in each layer are not prescribed in advance: they are outputs of the algorithm. It is an algorithm for growth of the network, which adds layers, and units inside a layer, at will until convergence. The convergence is guaranteed and numerical tests of this strategy look promising.

Neural Networks and ApplicationsBlind Source Separation TechniquesFace and Expression RecognitionConvergence (economics)AlgorithmComputer scienceFeed forwardBoolean functionLayer (electronics)Function (biology)Boolean networkOrder (exchange)Theoretical computer science
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431
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41.93
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References
Learning algorithms with optimal stability in neural networks
Journal of Physics A Mathematical and General · 1987 · 423 citations
Principles of Neurodynamics.
American Mathematical Monthly · 1963 · 2,222 citations
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