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An unsupervised training connectionist network with lateral inhibition

  • Levente Kocsis
  • Nicolae B. Szirbik
3 Machine Learning Learning Advances in Neural Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1416)

Abstract

A new architecture for unsupervised learning is proposed. The topology, activation rules, and training algorithm are presented and a specific training base is used to prove the advantages of this type of network. The training patterns are from chess playing, but there are several other applications for this kind of system, and a specific one is proposed without going into details. Experimental results emphasize the performances of the network training.

Keywords

Lateral Inhibition Input Pattern Training Base Pattern Separation Training Epoch 
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

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Levente Kocsis
    • 1
  • Nicolae B. Szirbik
    • 1
  1. 1.Department of Computer ScienceTechnical University of TimisoaraTimisoaraRomania

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