A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition

Detalhes bibliográficos
Autor(a) principal: Batsaikan, O.
Data de Publicação: 2009
Outros Autores: Ho, C. K., Singh, Y. P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: INFOCOMP: Jornal de Ciência da Computação
Texto Completo: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244
Resumo: This paper proposes a hybrid genetic algorithm and support vector machine (GA-SVM) approach to address the Mongolian character recognition problem. As the character recognition problem can be considered as a multi-class classification problem, we devise a DAG-SVM classifier. DAG-SVM uses the One-Against-One technique to combine multiple binary SVM classifiers. The GA is used to select the multi-class SVM model parameters. Empirical results demonstrate that the GA-SVM approach is able to achieve good accuracy rate.
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spelling A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character RecognitionSupport vector machinesgenetic algorithmsclassificationThis paper proposes a hybrid genetic algorithm and support vector machine (GA-SVM) approach to address the Mongolian character recognition problem. As the character recognition problem can be considered as a multi-class classification problem, we devise a DAG-SVM classifier. DAG-SVM uses the One-Against-One technique to combine multiple binary SVM classifiers. The GA is used to select the multi-class SVM model parameters. Empirical results demonstrate that the GA-SVM approach is able to achieve good accuracy rate.Editora da UFLA2009-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244INFOCOMP Journal of Computer Science; Vol. 8 No. 1 (2009): March, 2009; 1-71982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244/229Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessBatsaikan, O.Ho, C. K.Singh, Y. P.2015-07-01T12:46:26Zoai:infocomp.dcc.ufla.br:article/244Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:26.948456INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
title A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
spellingShingle A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
Batsaikan, O.
Support vector machines
genetic algorithms
classification
title_short A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
title_full A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
title_fullStr A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
title_full_unstemmed A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
title_sort A Genetic Algorithm-based Multi-class Support Vector Machine for Mongolian Character Recognition
author Batsaikan, O.
author_facet Batsaikan, O.
Ho, C. K.
Singh, Y. P.
author_role author
author2 Ho, C. K.
Singh, Y. P.
author2_role author
author
dc.contributor.author.fl_str_mv Batsaikan, O.
Ho, C. K.
Singh, Y. P.
dc.subject.por.fl_str_mv Support vector machines
genetic algorithms
classification
topic Support vector machines
genetic algorithms
classification
description This paper proposes a hybrid genetic algorithm and support vector machine (GA-SVM) approach to address the Mongolian character recognition problem. As the character recognition problem can be considered as a multi-class classification problem, we devise a DAG-SVM classifier. DAG-SVM uses the One-Against-One technique to combine multiple binary SVM classifiers. The GA is used to select the multi-class SVM model parameters. Empirical results demonstrate that the GA-SVM approach is able to achieve good accuracy rate.
publishDate 2009
dc.date.none.fl_str_mv 2009-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244
url https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/244/229
dc.rights.driver.fl_str_mv Copyright (c) 2016 INFOCOMP Journal of Computer Science
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 INFOCOMP Journal of Computer Science
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editora da UFLA
publisher.none.fl_str_mv Editora da UFLA
dc.source.none.fl_str_mv INFOCOMP Journal of Computer Science; Vol. 8 No. 1 (2009): March, 2009; 1-7
1982-3363
1807-4545
reponame:INFOCOMP: Jornal de Ciência da Computação
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str INFOCOMP: Jornal de Ciência da Computação
collection INFOCOMP: Jornal de Ciência da Computação
repository.name.fl_str_mv INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv infocomp@dcc.ufla.br||apfreire@dcc.ufla.br
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