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This paper presents a hybrid neural network classifier of fuzzy ARTMAP (FAM) and the dynamic decay adjustment (DDA) algorithm. The proposed FAMDDA model is a conflict-resolving classifier that can perform stable and incremental learning while settling overlapping of hyper-rectangular prototypes of different classes in minimizing misclassification rates. The performance of FAMDDA is evaluated using...
This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning,...
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