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