Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-validation in Fuzzy ARTMAP for large databases
Neural Networks
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Managing category proliferation in fuzzy ARTMAP caused by overlapping classes
IEEE Transactions on Neural Networks
Self-organizing ARTMAP rule discovery
Neural Networks
IEEE Transactions on Neural Networks
μARTMAP: use of mutual information for category reduction in Fuzzy ARTMAP
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
ART-based fusion of multi-modal perception for robots
Neurocomputing
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The issue of category proliferation caused by the overlapping classes in fuzzy ARTMAP (FAM) is addressed in this paper. A new FAM-based neural architecture called TTPFAM is proposed, which can reduce category proliferation by performing a threshold filtering mechanism before a new category created during training, and improve the classification accuracy by combining prediction distributed by the dynamic Q-max rule and posterior probability estimated during testing. The TPPFAM can produce a small size of neural network architecture without degradation of the classification accuracy. The algorithm is evaluated in terms of the classification accuracy and the number of categories by experiments on both artificial and real data, and the results show that the performance of TPPFAM is better than that of the other models.