Managing category proliferation in fuzzy ARTMAP caused by overlapping classes
IEEE Transactions on Neural Networks
Fuzzy ARTMAP rule extraction in computational chemistry
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
Bayesian ARTMAP for regression
Neural Networks
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We introduce a new fuzzy ARTMAP (FAM) neural network: Fuzzy ARTMAP with relevance factor (FAMR). The FAMR architecture is able to incrementally "grow" and to sequentially accommodate input-output sample pairs. Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. The relevance factors are user-defined or computed. The FAMR can be trained as a classifier and, at the same time, as a nonparametric estimator of the probability that an input belongs to a given class. The FAMR probability estimation converges almost surely and in the mean square to the posterior probability. Our theoretical results also characterize the convergence rate of the approximation. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source. We analyze the FAMR capability for mapping noisy functions when training data originates from multiple sources with known levels of noise.