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Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeyBreveJúniMasc:2007:MuPeCl
TitleMultilayer Perceptron Classifier Combination for Identification of Materials on Noisy Soil Science Multispectral Images
FormatPrinted, On-line.
Access Date2021, Jan. 25
Number of Files1
Size261 KiB
Context area
Author1 Breve, Fabricio Aparecido
2 Júnior, Moacir Pereira Ponti
3 Mascarenhas, Nelson Delfino D'Ávila
Affiliation1 Universidade Federal de São Carlos
2 Universidade Federal de São Carlos
3 Universidade Federal de São Carlos
EditorFalcão, Alexandre Xavier
Lopes, Hélio Côrtes Vieira
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
DateOct. 7-10, 2007
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2007-07-09 18:41:05 :: -> administrator ::
2007-08-02 21:17:18 :: administrator -> ::
2008-07-17 14:09:42 :: -> administrator ::
2009-08-13 20:38:19 :: administrator -> banon ::
2010-08-28 20:02:26 :: banon -> administrator ::
2020-02-19 03:06:18 :: administrator -> :: 2007
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsclassifier combination multilayer perceptron dempster-shafer decision templates bagging soil tomograph images.
AbstractClassifier combination experiments using the Multilayer Perceptron (MLP) were carried out using noisy soil science multispectral images, which were obtained using a Tomograph scanner. Using few units in the MLP hidden layer, images were classified using a single classifier. Later we used classifier combining techniques as Bagging, Decision Templates (DT) and Dempster-Shafer (DS), in order to improve the performance of the single classifiers and also stabilize the performance of the Multilayer Perceptron. The classification results were evaluated using Cross-Validation. The results showed stabilization of Multilayer Perceptron and improved results were achieved with fewer units in the MLP hidden layer.
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