Category Art: a Variation on Adaptive Resonance Theory Neural Networks

Abstract

In this paper we describ� Category ART, a variation on the adaptive resonance theory (ART) neural network models. Category ART is a predictive ART architecture because it incorporates an ART module to be able to learn to predict a prescribed category given a prescribed n-dimensional input vector a. In contrast to ARTMAP, Category ART contains only one ART module and the map field algorithm has been simplified. The remaining ART module in a Categoty ART can be either Fuzzy ART or ART2-A. Its performance is demonstrated on a benchmark neural network test, the two spiraals problem.

Cite this paper

@inproceedings{Weenink1997CategoryAA, title={Category Art: a Variation on Adaptive Resonance Theory Neural Networks}, author={David Weenink}, year={1997} }