Survey of learning results in adaptive resonance theory (ART) architectures
Michael Georgiopoulos, J. Huang, Gregory L. Heileman · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In this paper we investigate the learning properties of ART1, Fuzzy Art, and ARTMAP architectures. These architectures were introduced by Carpenter and Grossberg over the last eight years. some of the learning properties discussed in this paper involve characteristics of the clusters formed in these architectures while other learning properties concentrate on how fast it will take these architectures to converge to a solution for the type of problems that are capable of solving. This latter issue is very important in the neural network literature, and there are very few instances where it has been answered satisfactorily.