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Classification Method by Using the Associative Memories in Cellular Neural Networks

  • Akihiro Kanagawa
  • Hiroaki Kawabata
  • Hiromitsu Takahashi
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

This paper deals with a classification problem, such as medical diag-nosis, which classes are defined by categorical forms. Classification should be done by careful and synthetical judgement for a lot of characteristic values taking each individual variations into account. We use the associative memory function of the cellular neural networks to classify by means of remembering one category from among the preregistered categories.

Keywords

Liver Cirrhosis Associative Memory Cellular Neural Network Diagnosis Problem Hopfield Network 
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. Fisher, R.A. (1936): The use of Multiple Mesurements in Taxonomic Problems, Annals of Eugenics, 7, pp. 175–188.Google Scholar
  2. Liu, D and Michel,A.N. (1993): Cellular Neural Networks for Associative Memories, IEEE Trans. Circuits Syst. II, 40, pp. 119–121.MATHCrossRefGoogle Scholar
  3. Pawlak, Z (1984): Rough Classification,lntern. J. of Man Machine Studies, 30, pp. 457–473.Google Scholar
  4. Shigenaga, T., Ishibuchi,H. and Tanaka, H. (1993): Fuzzy Inference of Expert System Based on Rough Sets and Its Application to Classification Problems,“ J. of Japan Society for Fuzzy Theory and Systems, 5, 2, pp. 358–366.Google Scholar

Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Akihiro Kanagawa
    • 1
  • Hiroaki Kawabata
    • 1
  • Hiromitsu Takahashi
    • 1
  1. 1.Faculty of Computer Science and System Engineering Okayama Prefectural UniversitySoja, OkayamaJapan

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