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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorKasparis, Takis-
dc.contributor.authorGeorgiopoulos, Michael N.-
dc.contributor.authorKoufakou, Anna-
dc.contributor.otherΚασπαρής, Τάκης-
dc.date.accessioned2013-02-18T08:58:21Zen
dc.date.accessioned2013-05-17T05:29:36Z-
dc.date.accessioned2015-12-02T12:06:55Z-
dc.date.available2013-02-18T08:58:21Zen
dc.date.available2013-05-17T05:29:36Z-
dc.date.available2015-12-02T12:06:55Z-
dc.date.issued2001-07-
dc.identifier.citationInternational Joint Conference on Neural Networks, 2001,Washingtonen_US
dc.identifier.issn1098-7576-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/2847-
dc.description.abstractWe 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 ARTMAPen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2001 IEEEen_US
dc.subjectNeural networksen_US
dc.subjectComputational complexityen_US
dc.subjectFuzzy setsen_US
dc.titleOvertraining in fuzzy ARTMAP: myth or reality?en_US
dc.typeConference Papersen_US
dc.affiliationUniversity of Central Floridaen
dc.collaborationUniversity of Central Floridaen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceIEEE International Conference on Neural Networksen_US
dc.identifier.doi10.1109/IJCNN.2001.939529en_US
dc.dept.handle123456789/54en
cut.common.academicyear2000-2001en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-3486-538x-
crisitem.author.parentorgFaculty of Engineering and Technology-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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