Please use this identifier to cite or link to this item:
Title: Overtraining in fuzzy ARTMAP: myth or reality?
Authors: Kasparis, Takis 
Georgiopoulos, Michael N.
Koufakou, Anna
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
ISSN: 10987576
DOI: 10.1109/IJCNN.2001.939529
Rights: © 2001 IEEE
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s)

Last Week
Last month
checked on Dec 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.