A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Decision-based neural networks with signal/image classification applications
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
Agent-Based Approach to Distributed Ensemble Learning of Fuzzy ARTMAP Classifiers
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
Electric load forecasting using a fuzzy ART&ARTMAP neural network
Applied Soft Computing
Managing category proliferation in fuzzy ARTMAP caused by overlapping classes
IEEE Transactions on Neural Networks
An adaptive multiobjective approach to evolving ART architectures
IEEE Transactions on Neural Networks
Internal categories with irregular geometry and overlapping in ART networks
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt.A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy.As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.