A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
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We introduce a novel Fuzzy ARTMAP (FAM) architecture: FAM with Feature Weighting (FAMFW). In the first stage, the features of the training data are weighted. In the second stage, the obtained weights are used to improve the FAMFW training. The effect of this approach is a more sensitive FAM category determination: Category dimensions in the direction of relevant features are decreased whereas category dimensions in the direction of non-relevant feature are increased. Potentially, any feature weighting method could be used, which makes the FAMFW very general. In our study, we use a feature weighting algorithm based on the Neural-Gas algorithm.