Title :
Constrained optimization in pattern classification using ART
Author :
Yang, Shuyu ; Mitra, Sunanda
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Tech. Univ., Lubbock, TX, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
Pattern classification problems involve constrained optimization in the form of minimization of chosen cost functions. Such embedded constraints in integrated neural-fuzzy pattern recognition systems improve the performance of these systems. The comparative performance of an ART-based pattern classifier integrated with fuzzy optimization constraints is demonstrated in designing vector quantizers
Keywords :
ART neural nets; constraint theory; fuzzy neural nets; minimisation; pattern classification; vector quantisation; ART neural nets; constrained optimization; embedded constraints; fuzzy optimization constraints; minimization; neural-fuzzy pattern recognition systems; pattern classification; vector quantizer design; Algorithm design and analysis; Clustering algorithms; Constraint optimization; Fuzzy logic; Neural networks; Pattern classification; Pattern recognition; Subspace constraints; Testing; Vector quantization;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007567
