A fuzzy hybrid model for pattern classification
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.
KeywordsDecision Tree Membership Function Fuzzy Cluster Input Pattern Training Pattern
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