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
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;
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-8186-7282-X
DOI :
10.1109/ICPR.1996.547665
