Symbolic Pattern Classifiers Based on the Cartesian System Model

  • Manabu Ichino
  • Hiroyuki Yaguchi
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


As symbolic pattern classifiers, this paper presents region oriented methods based on the Cartesian system model which is a mathematical model to treat symbolic data. Our region oriented methods are able to use locally effective information to discriminate between pattern classes. This fact may achieve, at least superficially, a perfect discrimination of the pattern classes under a finite design set. Therefore, we have to take a ballance between the separability between classes and the generality of class desciptions. We describe this viewpoint theoretically and experimentally in order to assert the importance of feature selection which is essentially important in any pattern classification problem. We present also an example based on symbolic data in order to illustrate the usefulness of our approach.


Feature Selection Feature Space Pattern Classification Euclidean Plane Symbolic Data 


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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • Manabu Ichino
    • 1
  • Hiroyuki Yaguchi
    • 1
  1. 1.Tokyo Denki UniversityHatoyama, SaitamaJapan

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