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Τίτλος: Cross-validation in Fuzzy ARTMAP for large databases
Συγγραφείς: Koufakou, Anna 
Georgiopoulos, Michael N. 
Kasparis, Takis 
metadata.dc.contributor.other: Κασπαρής, Τάκης
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Accuracy;Neural networks;Fuzzy logic;Computer software--Validation
Ημερομηνία Έκδοσης: Νοε-2001
Πηγή: Neural Networks, 2001, vol. 14, no. 9, pp. 1279–1291
Volume: 14
Issue: 9
Start page: 1279
End page: 1291
Περιοδικό: Neural Networks 
Περίληψη: 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: cross-validation. In our experiments, we compare the performance of Fuzzy ARTMAP that is trained (i) until the completion of training, (ii) for one epoch, and (iii) until its performance on a validation set is maximized. The experiments were performed on artificial and real databases. The conclusion derived from those 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/20.500.14279/1675
ISSN: 08936080
DOI: 10.1016/S0893-6080(01)00090-9
Rights: © Elsevier
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Affiliation: University of Central Florida 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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