Title :
ART-C: a neural architecture for self-organization under constraints
Author :
He, Ji ; Tan, Ah-Hwee ; Tan, Chew-Lim
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore
fDate :
6/24/1905 12:00:00 AM
Abstract :
Proposes an ART-based neural architecture known as ART-C (ART under constraints) that performs online clustering of pattern sequences subject to the constraints on the recognition category representation. Experiments on two real-life data sets show that ART-C produces reasonably good clustering qualities, with the added advantage of high efficiency
Keywords :
ART neural nets; computational complexity; fuzzy neural nets; learning (artificial intelligence); pattern clustering; self-organising feature maps; ART-C; constraints; machine learning; neural architecture; online clustering; pattern sequences; recognition category representation; self-organization; Computer architecture; Constraint theory; Content management; Encoding; Helium; Machine learning; Neural networks; Pattern recognition; Resonance; Subspace constraints;
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.2002.1007545
