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Cross-validation in fuzzy ARTMAP neural networks for large sample classification problems
Applications and Science of Computational Intelligence IV
In this paper we are examining the issue of overtraining in Fuzzy ARTMAP. Over-training in Fuzzy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses, and (b) it creates unnecessarily large Fuzzy ARTMAP neural network architectures. In this work we are demonstrating that overtraining happens in Fuzzy ARTMAP and we propose an old remedy for its cure: crossvalidation. In our experiments we compare the performance ofdoi:10.1117/12.421155 fatcat:cxpgkwxdpbgdxdojkp5oiziumm