TY - GEN
T1 - Properties of learning of a fuzzy ART variant
AU - Georgiopoulos, M.
AU - Dagher, I.
AU - Heileman, G. L.
AU - Bebis, G.
N1 - Funding Information:
This research was supported in part by a grant from Boeing Computer Services under contract W-300445.
PY - 1997
Y1 - 1997
N2 - This paper discusses one variation of the fuzzy ART architecture, referred to as fuzzy ART variant. The fuzzy ART variant is a fuzzy ART algorithm, with a very large value for the choice parameter. Based on the geometrical interpretation of templates in fuzzy ART we present and prove useful properties of learning pertaining to the fuzzy ART variant. One of these properties of learning establishes an upper bound on the number of list presentations required by the fuzzy ART variant to learn an arbitrary list of input patterns presented to it. In previously published work, it was shown that the fuzzy ART variant performs as well as a fuzzy ART algorithm with more typical values for the choice parameter. Hence, the fuzzy ART variant is as good a clustering machine as the fuzzy ART algorithm using more typical values of the choice parameter.
AB - This paper discusses one variation of the fuzzy ART architecture, referred to as fuzzy ART variant. The fuzzy ART variant is a fuzzy ART algorithm, with a very large value for the choice parameter. Based on the geometrical interpretation of templates in fuzzy ART we present and prove useful properties of learning pertaining to the fuzzy ART variant. One of these properties of learning establishes an upper bound on the number of list presentations required by the fuzzy ART variant to learn an arbitrary list of input patterns presented to it. In previously published work, it was shown that the fuzzy ART variant performs as well as a fuzzy ART algorithm with more typical values for the choice parameter. Hence, the fuzzy ART variant is as good a clustering machine as the fuzzy ART algorithm using more typical values of the choice parameter.
UR - http://www.scopus.com/inward/record.url?scp=0030702531&partnerID=8YFLogxK
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U2 - 10.1109/ICNN.1997.614209
DO - 10.1109/ICNN.1997.614209
M3 - Conference contribution
AN - SCOPUS:0030702531
SN - 0780341228
SN - 9780780341227
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2012
EP - 2016
BT - 1997 IEEE International Conference on Neural Networks, ICNN 1997
T2 - 1997 IEEE International Conference on Neural Networks, ICNN 1997
Y2 - 9 June 1997 through 12 June 1997
ER -