A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Application of fuzzy ARTMAP and ART-EMAP to automatic target recognition using radar range profiles
Neural Networks - Special issue: automatic target recognition
Managing category proliferation in fuzzy ARTMAP caused by overlapping classes
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
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
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This paper presents a new neural network architecture called the Multilayer Fuzzy ARTMAP (ML-FAM) together with the neighborhood learning algorithm and N-best rule for fast learning and testing in solving pattern classification problem. An analysis to the Fuzzy ARTMAP learning algorithm is studied to identify the weakness of it. ML-FAM uses layered structure to seek for important region of the category in both learning and testing phase. Thus, its learning time and testing time are reduced. Besides that, N-best rule is introduced to improve the generalization performance of ML-FAM particularly for high dimensional problem by taking the advantage of layered structure of ML-FAM. Experimental results show that ML-FAM is superior to Fuzzy ARTMAP in terms of learning time and testing time while preserving the generalization capability.