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Reference TypeConference Paper (Conference Proceedings)
Last Update2005: (UTC) administrator
Metadata Last Update2020: (UTC) administrator
Citation KeyBrevePontMasc:2005:CoMeSt
TitleCombining methods to stabilize and increase performance of neural network-based classifiers
Access Date2021, Nov. 27
Number of Files1
Size335 KiB
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Author1 Breve, Fabricio Aparecido
2 Ponti Junior, Moacir Pereira
3 Mascarenhas, Nelson Delfino d'Ávila
Affiliation1 Departamento de Computação – Universidade Federal de São Carlos, São Paulo, SP, Brasil
EditorRodrigues, Maria Andréia Formico
Frery, Alejandro César
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
Conference LocationNatal
Date9-12 Oct. 2005
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:10:59 :: fbreve -> banon ::
2008-08-26 15:17:01 :: banon -> administrator ::
2009-08-13 20:37:45 :: administrator -> banon ::
2010-08-28 20:01:17 :: banon -> administrator ::
2020-02-19 03:19:09 :: administrator -> :: 2005
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Content TypeExternal Contribution
Keywordsclassifier combining neural networks multilayer perceptron dempster-shafer decision templates bagging pattern recognition soil science tomography
AbstractIn this paper we present a set of experiments in order to recognize materials in multispectral images, which were obtained with a tomograph scanner. These images were classified by a neural network based classifier (Multilayer Perceptron) and classifier combining techniques (Bagging, Decision Templates and Dempster-Shafer) were investigated. We also present a performance comparison between the individual classifiers and the combiners. The results were evaluated by the estimated error (obtained using the Hold-Out technique) and the Kappa coefficient, and they showed performance stabilization.
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