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Multicategory Classification Based on the Hypercube Self-Organizing Mapping (SOM) Scheme

  • Lan Du
  • Junying Zhang
  • Zheng Bao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

A new multicalss recognition strategy is proposed in this paper, where the self-organizing mapping (SOM) scheme with a hypercube mapped space is used to represent each category in a binary string format and a binary classifier is assigned to each bit in the string. Our strategy outperforms the existing approaches in the prior knowledge requirement, the number of binary classifiers, computation complexity, storage requirement, decision boundary complexity and recognition rate.

Keywords

Recognition Rate Cluster Center Decision Boundary Binary Classifier Test Calibration 
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.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons, New YorkGoogle Scholar
  2. 2.
    Xu, L., Krzyżak, A., Oja, E.: Rival Penalized Competitive Learning for Clustering Analysis, RBF Net, and Curve Detection. IEEE Transaction on N.N. 4, 636–648Google Scholar
  3. 3.
    Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: A review. IEEE Trans. on P.A.M.I. 22(1), 4–37Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lan Du
    • 1
  • Junying Zhang
    • 1
  • Zheng Bao
    • 1
  1. 1.National Lab. of Radar Signal ProcessingXidian UniversityShaanxiChina

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