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https://hdl.handle.net/20.500.14279/2847
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Kasparis, Takis | - |
dc.contributor.author | Georgiopoulos, Michael N. | - |
dc.contributor.author | Koufakou, Anna | - |
dc.contributor.other | Κασπαρής, Τάκης | - |
dc.date.accessioned | 2013-02-18T08:58:21Z | en |
dc.date.accessioned | 2013-05-17T05:29:36Z | - |
dc.date.accessioned | 2015-12-02T12:06:55Z | - |
dc.date.available | 2013-02-18T08:58:21Z | en |
dc.date.available | 2013-05-17T05:29:36Z | - |
dc.date.available | 2015-12-02T12:06:55Z | - |
dc.date.issued | 2001-07 | - |
dc.identifier.citation | International Joint Conference on Neural Networks, 2001,Washington | en_US |
dc.identifier.issn | 1098-7576 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/2847 | - |
dc.description.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 | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © 2001 IEEE | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Computational complexity | en_US |
dc.subject | Fuzzy sets | en_US |
dc.title | Overtraining in fuzzy ARTMAP: myth or reality? | en_US |
dc.type | Conference Papers | en_US |
dc.affiliation | University of Central Florida | en |
dc.collaboration | University of Central Florida | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.country | United States | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | IEEE International Conference on Neural Networks | en_US |
dc.identifier.doi | 10.1109/IJCNN.2001.939529 | en_US |
dc.dept.handle | 123456789/54 | en |
cut.common.academicyear | 2000-2001 | en_US |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.cerifentitytype | Publications | - |
item.openairetype | conferenceObject | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-3486-538x | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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