Application of fuzzy ARTMAP and ART-EMAP to automatic target recognition using radar range profiles
Neural Networks - Special issue: automatic target recognition
Applications of Neural Networks in Electromagnetics
Applications of Neural Networks in Electromagnetics
MicroARTMAP: Use of Mutual Information for Category Reduction in Fuzzy ARTMAP
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Voting Algorithm of Fuzzy ARTMAP and Its Application to Fault Diagnosis
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Incremental rule pruning for fuzzy ARTMAP neural network
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
IEEE Transactions on Neural Networks
Fuzzy ARTMAP with input relevances
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Multilayer Fuzzy ARTMAP: fast learning and fast testing for pattern classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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This paper addresses the difficulties brought about by overlapping classes in fuzzy ARTMAP (FAM). Training with such data leads to category proliferation, and classification is made difficult not only by the large number of categories but also the fact that such data can belong to either class. In this paper, changes were proposed to allow more than one class to be predicted during classification, and a number of modifications were explored to reduce the number of categories. The excessive creation of small categories was suppressed with the implementation of the modifications, and the predictive accuracy improved despite the significant reduction in number of categories. No major changes needed to be made to the FAM architecture.