DocumentCode :
305390
Title :
Study on feature weight and feature selection in pattern classification neural networks
Author :
Li, Rui-Ping ; Mukaidono, Masao ; Turksen, I. Burhan
Author_Institution :
Dept. of Ind. Eng., Toronto Univ., Ont., Canada
Volume :
3
fYear :
1996
fDate :
14-17 Oct 1996
Firstpage :
1972
Abstract :
Pattern recognition have two major topics: 1) pattern classification; and 2) feature selection. Study on the later is seldom been found from published papers. This paper considers these two problems at the same time. We design an artificial neural network for pattern classification and feature selection. We also introduce our design thought in detailed. Two examples are used to illustrate our approach
Keywords :
Hebbian learning; feature extraction; fuzzy logic; fuzzy neural nets; pattern classification; feature selection; feature weight; fuzzy logic; fuzzy neural networks; neural networks; pattern classification; proportional learning vector quantisation; Artificial neural networks; Computer industry; Fuzzy set theory; Fuzzy sets; Humans; Linear discriminant analysis; Neural networks; Neurons; Pattern classification; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1996., IEEE International Conference on
Conference_Location :
Beijing
ISSN :
1062-922X
Print_ISBN :
0-7803-3280-6
Type :
conf
DOI :
10.1109/ICSMC.1996.565427
Filename :
565427
Link To Document :
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