Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43213
Title: ART-C: A neural architecture for self-organization under constraints
Authors: He, J. 
Tan, A.-H. 
Tan, C.-L. 
Keywords: Adaptive resonance theory
Constraint clustering
Machine learning
Issue Date: 2002
Source: He, J.,Tan, A.-H.,Tan, C.-L. (2002). ART-C: A neural architecture for self-organization under constraints. Proceedings of the International Joint Conference on Neural Networks 3 : 2550-2555. ScholarBank@NUS Repository.
Abstract: This paper proposes a novel 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.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/43213
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