Advertisement

Efficient learning in Multi-Layered Perceptron using the Grow-And-Learn algorithm

  • Gildas Cherruel
  • Bassel Solaiman
  • Yvon Autret
Posters Theory of Computation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 990)

Abstract

The well-known Multi-Layered Perceptron has gained power thanks to the Back Propagation Algorithm. The difficulty which still subsists is its time-wasting. In fact, the learning process can be improved by using the Grow-And-Learn (GAL) algorithm. In this paper, we present such a hybrid system: the cooperation between GAL and MLP networks. The obtained system is more rapid and more efficient than the classic Back Propagation which computes on the MLP.

Keywords

character recognition co-operation neural networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Y. L. Cun, “Generalization and network design strategies,” technical report crg-tr-89-4, University of Toronto, 1989.Google Scholar
  2. 2.
    N. Ohnishi, A. Okamoto, and N. Sugie, “Selective presentation of learning samples for efficient learning in multi-layered perceptron,” Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 278–289, Jan. 1990.Google Scholar
  3. 3.
    E. Alpaydin, “Grow-And-Learn: an incremental method for category learning,” Proceedings of the International Conference on Neural Networks, pp. 761–764, July 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Gildas Cherruel
    • 1
    • 2
  • Bassel Solaiman
    • 3
  • Yvon Autret
    • 1
  1. 1.Laboratoire LIMIUniversité de Bretagne OccidentaleBrest CedexFrance
  2. 2.TNIBrestFrance
  3. 3.Département Image et Traitement de l'InformationENSTBBrest CedexFrance

Personalised recommendations