DocumentCode :
2972546
Title :
Fuzzy pocket algorithm: a generalized pocket algorithm for classification of fuzzy inputs
Author :
Lee, Hahn-Ming ; Wang, Weng-Tang
Author_Institution :
Dept. of Electron. Eng., Nat. Taiwan Inst. of Technol., Taipei, Taiwan
Volume :
3
fYear :
1993
fDate :
25-29 Oct. 1993
Firstpage :
2873
Abstract :
Perceptron algorithm has been widely adopted in pattern recognition to decide linear decision boundaries. Pocket algorithm, a perceptron-based algorithm, works well with nonseparable or even contradictory training instances. In this paper, a generalized pocket algorithm, called fuzzy pocket algorithm, that is capable of handling inputs in linguistics terms is proposed. Linguistic terms are represented as LR-type fuzzy sets. LR-type fuzzy sets operations and defuzzification method are utilized. The fuzzy pocket algorithm is suitable of both fuzzy and crisp inputs. Besides, nodes needed for a linguistic term are few and computation load is light. One sample problem, called knowledge-based evaluator, is considered to illustrate the working of the proposed method. Also, the experimental results are very encouraging.
Keywords :
fuzzy set theory; pattern classification; perceptrons; LR-type fuzzy sets; classification; contradictory training instances; defuzzification method; fuzzy inputs; fuzzy pocket algorithm; knowledge-based evaluator; linear decision boundaries; linguistic inputs; nonseparable training instances; pattern recognition; perceptron algorithm; Classification algorithms; Computational modeling; Electronic mail; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Learning systems; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
Type :
conf
DOI :
10.1109/IJCNN.1993.714322
Filename :
714322
Link To Document :
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