DocumentCode
2530520
Title
GA-based pattern classification: theoretical and experimental studies
Author
Bandyopadhyay, S. ; Murthy, C.A. ; Pal, S.K.
Author_Institution
Machine Intelligence Unit, Indian Stat. Inst., Calcutta, India
Volume
4
fYear
1996
fDate
25-29 Aug 1996
Firstpage
758
Abstract
Merits of genetic algorithms (GAs), an efficient evolutionary searching paradigm, are utilized for pattern classification in ℜN by fitting hyperplanes to model the decision boundaries in the feature space. Theoretical analysis establishes that as the size of the training set (n) goes towards infinity, the error probability and the decision boundary of the GA based classifier will approach those of Bayes (optimum) classifier
Keywords
error statistics; feature extraction; genetic algorithms; learning (artificial intelligence); multilayer perceptrons; pattern classification; probability; search problems; decision boundaries; error probability; evolutionary searching; feature space; genetic algorithms; hyperplanes; learning; multilayer perceptrons; pattern classification; Genetic algorithms; H infinity control; Large Hadron Collider; Pattern classification; Training data; Wheels;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.547665
Filename
547665
Link To Document