Vigilance Adaptation in Adaptive Resonance Theory
Despite the advantages of fast and stable learning, Adaptive Resonance Theory (ART) still relies on an empirically fixed vigilance parameter value to determine the vigilance regions of all of the clusters in the category field (F 2), causing its performance to depend on the vigilance value. It would be desirable to use different values of vigilance for different category field nodes, in order to fit the data with a smaller number of categories. We therefore introduce two methods, the Activation Maximization Rule (AMR) and the Confliction Minimization Rule (CMR). Despite their differences, both ART with AMR (AM-ART) and with CMR (CM-ART) allow different vigilance levels for different clusters, which are incrementally adapted during the clustering process. Specifically, AMR works by increasing the vigilance value of the winner cluster when a resonance occurs and decreasing it when a reset occurs, which aims to maximize the participation of clusters for activation. On the other hand, after receiving an input pattern, CMR first identifies all of the winner candidates that satisfy the vigilance criteria and then tunes their vigilance values to minimize conflicts in the vigilance regions. In this paper, we chose Fuzzy ART to demonstrate these concepts, but they will clearly carry over to other ART architectures. Our comparative experiments show that both AM-ART and CM-ART improve the robust performance of Fuzzy ART to the vigilance parameter and usually produce better cluster quality.
L. Meng et al., "Vigilance Adaptation in Adaptive Resonance Theory," Proceedings of International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), Jan 2013.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2013.6706857
The 2013 International Joint Conference on Neural Networks (IJCNN) (2013: Aug. 4-9, Dallas, TX)
Electrical and Computer Engineering
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