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dc.contributor.authorDagher, Issamen_US
dc.descriptionThis paper was presented in " 8th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2004) .July 18-21, 2004. Orlando, USA. ".en_US
dc.description.abstractIn this paper, an L-p based Fuzzy ARTMAP neural network is presented. The category choice of this network is based on the L-p norm. Geometrical properties of this architecture are presented. Comparisons between this category choice and the category choice of the Fuzzy ARTMAP are illustrated. And simulation results on the databases taken from the UCI repository are performed. It will be shown that using the L-p norm is geometrically more attractive. It will operate directly on the input patterns without the need for doing any preprocessing. It should be noted that the Fuzzy ARTMAP architecture requires two preprocessing steps: normalization and complement coding. Simulation results on different databases show the good generalization performance of the L-p Fuzzy ARTMAP compared to the performance of Fuzzy ARTMAP.en_US
dc.format.extent7 p.en_US
dc.subjectL-p normen_US
dc.subjectFuzzy ARTMAPen_US
dc.subjectNeural networken_US
dc.subjectCategory choiceen_US
dc.titleL-p Fuzzy ARTMAP neural network architectureen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.relation.ispartoftextJournal of soft computingen_US
dc.provenance.recordsourceOliben_US of Engineering-
Appears in Collections:Department of Computer Engineering
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