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Improving ARTMAP learning through variable vigilance
A Canuto1, M Fairhurst, G Howells
1Electronic Engineering Laboratory, University of Kent, Canterbury, Kent CT27NT, UK. amdc1@ukc.ac.uk
International Journal of Neural Systems
|February 20, 2002
Abstract:
This paper presents a mechanism to vary the vigilance parameter in the RePART fuzzy neural network. This mechanism helps to smooth out the problem of category proliferation which affects ARTMAP-based networks. Empirical experiments show that the use of variable vigilance improves the performance of the RePART model while, at the same time, requiring a less complex structure.

