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XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05)
Combining Methods to Stabilize and Increase Performance of Neural Network-Based Classifiers
Natal, Rio Grande do Norte, Brazil
October 09-October 12
ISBN: 0-7695-2389-7
ASCII Text
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Fabricio A. Breve, Moacir Jr. P. Ponti, Nelson D. A. Mascarenhas, "Combining Methods to Stabilize and Increase Performance of Neural Network-Based Classifiers," Graphics, Patterns and Images, SIBGRAPI Conference on, pp. 105-111, XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05), 2005.
 
 
BibTex
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@article{ 10.1109/SIBGRAPI.2005.19,
author = {Fabricio A. Breve and Moacir Jr. P. Ponti and Nelson D. A. Mascarenhas},
title = {Combining Methods to Stabilize and Increase Performance of Neural Network-Based Classifiers},
journal ={Graphics, Patterns and Images, SIBGRAPI Conference on},
volume = {0},
year = {2005},
issn = {1530-1834},
pages = {105-111},
doi = {http://doi.ieeecomputersociety.org/10.1109/SIBGRAPI.2005.19},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Graphics, Patterns and Images, SIBGRAPI Conference on
TI - Combining Methods to Stabilize and Increase Performance of Neural Network-Based Classifiers
SN - 1530-1834
SP105
EP111
A1 - Fabricio A. Breve,
A1 - Moacir Jr. P. Ponti,
A1 - Nelson D. A. Mascarenhas,
PY - 2005
KW - null
VL - 0
JA - Graphics, Patterns and Images, SIBGRAPI Conference on
ER -
 
Fabricio A. Breve, Universidade Federal de São Carlos
Moacir Jr. P. Ponti, Universidade Federal de São Carlos
Nelson D. A. Mascarenhas, Universidade Federal de São Carlos
In 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.
Citation:
Fabricio A. Breve, Moacir Jr. P. Ponti, Nelson D. A. Mascarenhas, "Combining Methods to Stabilize and Increase Performance of Neural Network-Based Classifiers," sibgrapi, pp.105-111, XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05), 2005
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