A fuzzy hybrid model for pattern classification

  • Prasenjit Biswas
  • Arun K. Majumdar
Fuzzy Set And Pattern Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


In this paper we propose a hybrid model for pattern classification. The model is hybrid in the sense that the first phase of the classifier uses a supervised learning algorithm for establishing the fuzzy separability of pattern classes based on the Fuzzy set theoretic approach producing hierarchical binary decision trees; and the second phase uses a syntactic approach. The effectiveness of the model is demonstrated by using it for recognition of handprinted Devanagri characters. The principle of classifier design described in this paper establishes a methodology for identifying the boundary of transition from the geometric to the structural approach in such hybrid classification schemes.


Decision Tree Membership Function Fuzzy Cluster Input Pattern Training Pattern 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Prasenjit Biswas
    • 1
  • Arun K. Majumdar
    • 2
  1. 1.Dept. of Computer Sc. and Engg.Southern Methodist Univ.DallasUSA
  2. 2.Dept. of Computer ScienceUniv. of GuelphGuelphCanada

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