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008			080998 th eng
035			_a.b10053852
099		9	_aAIT Thesis no.CS-94-16
100	1		_aRao, G. R. M. Sudhakara
245	1	0	_aAdaptive Resonance Theory (ART) and pattern clustering
260			_aBangkok : _bAsian Institute of Technology, _c1994
300			_a65 leaves
490	1		_aThesis ; _vno. CS-94-16
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering of Technology
502			_aThesis (M.Eng.) - Asian Institute of Technology, 1994
520			_aThis thesis investigates Adaptive Resonance Theory 1 (ARTl) as a pattern clustering algorithm. Inherent characteristics of the model are analyzed. In particular the vigilance parameter, p, and its role in classification of patterns is examined. The experiments show that the vigilance parameter as defined by Carpenter & Grossberg does not necessarily increase the number of categories with its value, but decrease also, against the claim made by them. Hence, the lemma, "Increasing p increases the total number of clusters learned and decreases the size of each cluster" stated by Barbara Moore (1989), an MIT AI researcher, is not always valid. A modified vigilance test criteria has been proposed, which takes into account, the problem of subset & superset patterns and stably categorize, arbitrarily many input patterns in one list presentation when the vigilance parameter is closer to one. The proposed method also performs much better with regard to classification of patterns and reduces the number of list presentations required for stable category learning. Better perfo1mance of new similarity criteria with regard to noisy patterns also has been shown with experimental results.
650		0	_aNeural networks (Computer science)
700	1		_aSadananda, Ramakoti, _eChairperson
700	1		_aMurai, Shunji, _eExamination Committee
700	1		_aYulu, Qi, _eExamination Committee
710	2		_aADB, Japan, _eScholaship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. CS-94-16
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B15671
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