Neural Networks
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
Self-Organization of Topographic Mixture Networks Using Attentional Feedback
Neural Computation
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Guaranteed two-pass convergence for supervised and inferential learning
IEEE Transactions on Neural Networks
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
GFAM: Evolving Fuzzy ARTMAP neural networks
Neural Networks
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Journal of Biomedical Imaging
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
Monitoring roundness profiles based on an unsupervised neural network algorithm
Computers and Industrial Engineering
How well Fuzzy ARTMAP approximates functions?
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper we introduce novel geometric concepts, namely category regions, in the original framework of Fuzzy-ART (FA) and Fuzzy-ARTMAP (FAM). The definitions of these regions are based on geometric interpretations of the vigilance test and the F2 layer competition of committed nodes with uncommitted ones, that we call commitment test. It turns out that not only these regions have the same geometrical shape (polytope structure), but they also share a lot of common and interesting properties that are demonstrated in this paper. One of these properties is the shrinking of the volume that each one of these polytope structures occupies, as training progresses, which alludes to the stability of learning in FA and FAM, a well-known result. Furthermore, properties of learning of FA and FAM are also proven utilizing the geometrical structure and properties that these regions possess; some of these properties were proven before using counterintuitive, algebraic manipulations and are now demonstrated again via intuitive geometrical arguments. One of the results that is worth mentioning as having practical ramifications is the one which states that for certain areas of the vigilance-choice parameter space (p,a), the training and performance (testing) phases of FA and FAM do not depend on the particular choices of the vigilance parameter. Finally, it is worth noting that, although the idea of the category regions has been developed under the premises of FA and FAM, category regions are also meaningful for later developed ART neural network structures, such as ARTEMAP, ARTMAP-IC, Boosted ARTMAP, Micro-ARTMAP, Ellipsoid-ART/ARTMAP, among others.