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ICANN ’93 pp 59-62 | Cite as

Improving Categorization with CALM Maps

  • Ed Lebert
  • R. Hans Phaf
Conference paper

Abstract

The Categorizing And Learning Module (CALM) represents different patterns on different nodes through a competitive learning procedure. We study an extension of CALM that enforces a topological structure on the representations. The main difference with Kohonen’s self-organizing feature map is that no external regulating mechanisms are needed to learn a stable map. Simulations show that this CALM Map, in comparison to the standard CALM module, improves categorization because the stretching property of CALM Maps enables a continuous process of separation, whereas CALM will eventually commit itself to a once obtained categorization.

Keywords

Learning Rate Elaboration Learning Modular Neural Network Inhibitory Weight Convergence Phase 
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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    E. Erwin, K. Obermayer, and K. Schulten. Self-organizing maps: Stationary states, metastability and convergence rate. Biological Cybernetics, 67: 47–55, 1992.CrossRefMATHGoogle Scholar
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    C.D. Gilbert and T.N. Wiesel. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. The Journal of Neuroscience, 9: 2432–2442, 1989.Google Scholar
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    T. Kohonen. Self-organization and associative memory. Springer-Verlag, Berlin, 2nd edition, 1988.CrossRefMATHGoogle Scholar
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    J.M.J. Murre. Learning and categorization in modular neural networks. Harvester Wheatsheaf, Hemel Hempstead, U.K., 1992.Google Scholar
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    J.M.J. Murre, R.H. Phaf, and G. Wolters. CALM: Categorizing and learning module. Neural Networks, 5: 55–82, 1992.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Ed Lebert
    • 1
  • R. Hans Phaf
    • 1
  1. 1.Psychonomics DepartmentUniversity of AmsterdamAmsterdamThe Netherlands

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