DocumentCode :
1805628
Title :
A fuzzy classifier based on probabilistic relaxation
Author :
Fu, Alan M N ; Yan, Hong
Author_Institution :
Dept. of Electr. & Inf. Eng., Sydney Univ., NSW, Australia
Volume :
6
fYear :
1999
fDate :
36342
Firstpage :
4351
Abstract :
In this paper, a new fuzzy classifier is developed in which a simple relation between probabilistic vectors and fuzzy sets is derived and the probabilistic relaxation scheme is employed. The fuzzy classifier consists of two stages. Firstly, the fuzzy sets are separated into several groups in terms of the relation between probabilistic vectors and fuzzy sets. Secondly, each group of fuzzy sets is further classified into different subgroups by the probabilistic relaxation scheme. Numerical experiments to verify the effectiveness of the proposed method are carried out. The results show that the method is simple and works well
Keywords :
fuzzy set theory; pattern classification; probability; relaxation theory; fuzzy classifier; fuzzy sets; probabilistic relaxation; probabilistic vectors; Australia; Clustering methods; Fuzzy neural networks; Fuzzy sets; Information processing; Labeling; Layout; Neural networks; Noise shaping; Random variables;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.830868
Filename :
830868
Link To Document :
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