Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2532
DC FieldValueLanguage
dc.contributor.authorKasparis, Takis-
dc.contributor.authorGeorgiopoulos, Michael N.-
dc.contributor.authorCharalampidis, Dimitrios-
dc.contributor.otherΚασπαρής, Τάκης-
dc.date.accessioned2013-02-18T12:51:27Zen
dc.date.accessioned2013-05-17T05:30:10Z-
dc.date.accessioned2015-12-02T11:35:12Z-
dc.date.available2013-02-18T12:51:27Zen
dc.date.available2013-05-17T05:30:10Z-
dc.date.available2015-12-02T11:35:12Z-
dc.date.issued2000-07-
dc.identifier.citationInternational Joint Conference on Neural Networks, 2000, Como, Italyen_US
dc.identifier.issn1098-7576-
dc.description.abstractThis paper describes an approach to classification of noisy signals using a technique based on the Fuzzy ARTMAP neural network (FAM). A variation of the testing phase of Fuzzy ARTMAP is introduced, that exhibited superior generalization performance than the standard Fuzzy ARTMAP in the presence of noise. We present an application of our technique for textured grayscale images. We perform a large number of experiments to verify the superiority of the modified over the standard Fuzzy ARTMAP. More specifically, the modified and the standard FAM were evaluated on two different sets of features (fractal-based and energy-based), for three different types of noise (Gaussian, uniform, exponential) and for two different texture sets (Brodatz, aerial). Furthermore, the classification performance of the standard and modified Fuzzy ARTMAP was compared for different network sizes.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2000 IEEEen_US
dc.subjectNeural networksen_US
dc.subjectFractalsen_US
dc.subjectFuzzy setsen_US
dc.subjectImage analysisen_US
dc.titleClassification of noisy signals using fuzzy ARTMAP neural networksen_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.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceInternational Joint Conference on Neural Networksen_US
dc.identifier.doi10.1109/IJCNN.2000.859372en_US
dc.dept.handle123456789/54en
cut.common.academicyear1999-2000en_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-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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