Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2532
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kasparis, Takis | - |
dc.contributor.author | Georgiopoulos, Michael N. | - |
dc.contributor.author | Charalampidis, Dimitrios | - |
dc.contributor.other | Κασπαρής, Τάκης | - |
dc.date.accessioned | 2013-02-18T12:51:27Z | en |
dc.date.accessioned | 2013-05-17T05:30:10Z | - |
dc.date.accessioned | 2015-12-02T11:35:12Z | - |
dc.date.available | 2013-02-18T12:51:27Z | en |
dc.date.available | 2013-05-17T05:30:10Z | - |
dc.date.available | 2015-12-02T11:35:12Z | - |
dc.date.issued | 2000-07 | - |
dc.identifier.citation | International Joint Conference on Neural Networks, 2000, Como, Italy | en_US |
dc.identifier.issn | 1098-7576 | - |
dc.description.abstract | This 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.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © 2000 IEEE | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Fractals | en_US |
dc.subject | Fuzzy sets | en_US |
dc.subject | Image analysis | en_US |
dc.title | Classification of noisy signals using fuzzy ARTMAP neural networks | 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.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | International Joint Conference on Neural Networks | en_US |
dc.identifier.doi | 10.1109/IJCNN.2000.859372 | en_US |
dc.dept.handle | 123456789/54 | en |
cut.common.academicyear | 1999-2000 | 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 | - |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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