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Learning in feed-forward neural networks by improving the performance

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  • Gordon, Mirta B.
  • Pereto, Pierre
  • Rodriguez-Girones, Miguel

Abstract

Statistical mechanics is used to derive a new learning rule for a feed-forward neural network with one hidden layer. Generalization to multilayer neural networks is straightforward, and proceeds in the same way as backpropagation.

Suggested Citation

  • Gordon, Mirta B. & Pereto, Pierre & Rodriguez-Girones, Miguel, 1992. "Learning in feed-forward neural networks by improving the performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 402-410.
    • Handle: RePEc:eee:phsmap:v:185:y:1992:i:1:p:402-410
    DOI: 10.1016/0378-4371(92)90481-5
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    References listed on IDEAS

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    1. Serafino Marchese & Francisco Simone, 1989. "Monotonicity of indices of “Revealed“ comparative advantage: Empirical evidence on Hillman’s condition," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 125(1), pages 158-167, March.
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