IMPROVING ARTMAP LEARNING THROUGH VARIABLE VIGILANCE
Anne M. P. Canuto, Michael C. Fairhurst, Gareth Howells · International Journal of Neural Systems · 2001
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.