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Reference TypeConference Paper (Conference Proceedings)
Last Update2012: administrator
Metadata Last Update2013: administrator
Citation KeyCostaJooKöbe:1993:DiNeNe
TitleDistance-discriminator neural networks for classification and pattern recognition
FormatImpresso, On-line.
Access Date2021, Mar. 07
Number of Files1
Size5635 KiB
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Author1 Costa, Luciano da Fontoura
2 Joo, Javier Montenegro
3 Köberle, Roland
Affiliation1 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
2 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
3 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
EditorFigueiredo, Luiz Henrique de
Gomes, Jonas de Miranda
Conference NameSimpósio Brasileiro de Computação Gráfica e Processamento de Imagens, 6 (SIBGRAPI)
Conference LocationRecife
Date19 - 22 out. 1993
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleAnais
Tertiary TypeArtigo
History2012-12-17 13:52:37 :: -> administrator ::
2013-01-03 01:24:55 :: administrator -> :: 1993
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Is the master or a copy?is the master
Content Stagecompleted
Keywordsdiscriminator neural, Distance-discriminator neurons, Distance-discriminator Neural Networks, interfaces.
AbstractDistance-discriminator neurons DDNs and their combination in Distance-discriminator Neural Networks DDNNs are proposed and discussed. DDNs, based on distance metric concepts, are able to discriminate whether a given point (x,y) belongs to a closed region such as diamond-, rectangle and ellipse-bound regions, which are tasks traditionally performed by perceptrons. DDNs can also be straightforwardly modified in order to discriminate hollow regions having as outer boundaries the above mentioned geometrical figures or even combinations of them. The principal advantage of DDNNs over perceptrons is a substantial reduction of execution time and/or the amount of required hardware operators: many polygonal classification regions which would otherwise demand large perceptron structures can be discriminated with only a few DDNNs. DDNNs can also be easily programmed by design or automatically with the help of the hough transform. Such issues as well as the relative advantages of DDNNs and perceptrons and a complete application example are presented and discussed in the present paper.
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