Convergence properties of a fuzzy ARTMAP network

Abstract

FAMR (Fuzzy ARTMAP with Relevance factor) is a FAM (Fuzzy ARTMAP) neural network used for classification, probability estimation [3], [2], and function approximation [4]. FAMR uses a relevance factor assigned to each sample pair, proportional to the importance of that pair during the learning phase. Due to its incremental learning capability, FAMR can efficiently process large data sets and is an appropriate tool for data mining applications. We present new theoretical results characterizing the stochastic convergence of FAMR