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|Title:||Overtraining in fuzzy ARTMAP: myth or reality?||Authors:||Kasparis, Takis
Georgiopoulos, Michael N.
|Keywords:||Neural networks;Computational complexity;Fuzzy sets||Issue Date:||2001||Publisher:||IEEE||Source:||International Joint Conference on Neural Networks, 2001,Washington||Abstract:||We examine the issue of overtraining in fuzzy ARTMAP. Over-training in fuzzy ARTMAP manifests itself in two different ways: 1) it degrades the generalization performance of fuzzy ARTMAP as training progresses; and 2) it creates unnecessarily large fuzzy ARTMAP neural network architectures. In this work we demonstrate that overtraining happens in fuzzy ARTMAP and propose an old remedy for its cure: cross-validation. In our experiments we compare the performance of fuzzy ARTMAP that is trained: 1) until the completion of training, 2) for one epoch, and 3) until its performance on a validation set is maximized. The experiments were performed on artificial and real databases. The conclusion derived from these experiments is that cross-validation is a useful procedure in fuzzy ARTMAP, because it produces smaller fuzzy ARTMAP architectures with improved generalization performance. The trade-off is that cross-validation introduces additional computational complexity in the training phase of fuzzy ARTMAP||URI:||http://ktisis.cut.ac.cy/handle/10488/7137||ISSN:||10987576||DOI:||10.1109/IJCNN.2001.939529||Rights:||© 2001 IEEE||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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