Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2847
Title: Overtraining in fuzzy ARTMAP: myth or reality?
Authors: Kasparis, Takis 
Georgiopoulos, Michael N. 
Koufakou, Anna 
metadata.dc.contributor.other: Κασπαρής, Τάκης
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Neural networks;Computational complexity;Fuzzy sets
Issue Date: Jul-2001
Source: International Joint Conference on Neural Networks, 2001,Washington
Conference: IEEE International Conference on Neural Networks 
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: https://hdl.handle.net/20.500.14279/2847
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2001.939529
Rights: © 2001 IEEE
Type: Conference Papers
Affiliation: University of Central Florida 
Affiliation : University of Central Florida 
Publication Type: Peer Reviewed
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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