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Generic Back-Propagation in Arbitrary FeedForward Neural Networks

  • Cédric Gégout
  • Bernard Girau
  • Fabrice Rossi

Abstract

In this paper, we describe a general mathematical model for feedforward neural networks. The final form of the network is a vectorial function f of two variables, x (the input of the network) and w (the weight vector). We show that the differential of f can be computed with an extended back-propagation algorithm or with a direct method. By evaluating the time needed to compute the differential with the help of both methods, we show how to chose the best one. We introduce also input sharing and output function which allow us to implement efficiently a multilayer perceptron with our model.

Keywords

Neural Network Weight Vector Input Space Feedforward Neural Network Weight Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Léon Bottou and Patrick Gallinari. A Framework for the Cooperation of Learning Algorithms. In R.P. Lippmann, J.E. Moody, and D.S. Touretzky, editors, Neural Information Processing Systems, volume 3, pages 781–788. Morgan Kauffman, 1991.Google Scholar
  2. [2]
    Cédric Gégout, Bernard Girau, and Fabrice Rossi. Les réseaux d’opérateurs. Technical report, Thomson- CSF/SDC, Juillet 1993.Google Scholar
  3. [3]
    Cédric Gégout, Bernard Girau, and Fabrice Rossi. NSK, an Object-Oriented Simulator Kernel for Arbitrary Feedforward Neural Networks. In Int. Conf on Tools with Artificial Intelligence, pages 93–104, New Orleans (Louisiana), November 1994. IEEE.Google Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • Cédric Gégout
    • 1
    • 2
    • 3
  • Bernard Girau
    • 2
  • Fabrice Rossi
    • 1
    • 3
  1. 1.École Normale Supérieure de ParisParisFrance
  2. 2.École Normale Supérieure de Lyon L.I.P.LyonFrance
  3. 3.THOMSON-CSF SDC/DPR/R4BagneuxFrance

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