Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation

In this work a new methodology for automatic selection of the free parameters in the Least Squares–Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does...

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Autores:
Álvarez-Meza, Andrés Marino
Daza Santacoloma, Genaro
Acosta Mejia, Carlos
Castallanos Dominguez, German
Tipo de recurso:
Article of journal
Fecha de publicación:
2012
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/31045
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/31045
http://bdigital.unal.edu.co/21121/
Palabra clave:
Informatics
Electrical and Electronic Engineering
Parameter selection
Least Squares-Support Vector Machines
Multidimensional Generalized Cross Validation
Regression.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
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repository_id_str
spelling Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Álvarez-Meza, Andrés Marino3bdb608d-4c13-42ce-8ac3-75224e8235ae300Daza Santacoloma, Genaro521d7f84-9b23-4c8e-a2d1-aae7fc6f62ce300Acosta Mejia, Carlos6af288ff-fed5-4cf4-b8d2-c447a3a4a4d6300Castallanos Dominguez, German531ae971-5d69-40b0-8fa0-486d5e66e86d3002019-06-26T14:19:28Z2019-06-26T14:19:28Z2012https://repositorio.unal.edu.co/handle/unal/31045http://bdigital.unal.edu.co/21121/In this work a new methodology for automatic selection of the free parameters in the Least Squares–Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does not require a prior knowledge about the influence of the LS-SVM free parameters in the results. The methodology is tested on two artificial and two real-world data sets. According to the results our methodology computes suitable regressions with competitive relative errors.application/pdfspaUniversidad Nacional de Colombia Sede Medellínhttp://revistas.unal.edu.co/index.php/dyna/article/view/17407Universidad Nacional de Colombia Revistas electrónicas UN DynaDynaDyna; Vol. 79, núm. 171 (2012); 23-30 DYNA; Vol. 79, núm. 171 (2012); 23-30 2346-2183 0012-7353Álvarez-Meza, Andrés Marino and Daza Santacoloma, Genaro and Acosta Mejia, Carlos and Castallanos Dominguez, German (2012) Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation. Dyna; Vol. 79, núm. 171 (2012); 23-30 DYNA; Vol. 79, núm. 171 (2012); 23-30 2346-2183 0012-7353 .Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validationArtículo de revistainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/ARTInformaticsElectrical and Electronic EngineeringParameter selectionLeast Squares-Support Vector MachinesMultidimensional Generalized Cross ValidationRegression.ORIGINAL17407-158373-1-PB.htmltext/html37469https://repositorio.unal.edu.co/bitstream/unal/31045/1/17407-158373-1-PB.html34cae09d96e6267de3506dd259e5edeaMD5117407-106197-1-PB.pdfapplication/pdf2345595https://repositorio.unal.edu.co/bitstream/unal/31045/2/17407-106197-1-PB.pdf3c23a42185fc89fa6e9074ad4c40d970MD52THUMBNAIL17407-106197-1-PB.pdf.jpg17407-106197-1-PB.pdf.jpgGenerated Thumbnailimage/jpeg9317https://repositorio.unal.edu.co/bitstream/unal/31045/3/17407-106197-1-PB.pdf.jpgb5693fe16be14702ba969d51ffb39cb4MD53unal/31045oai:repositorio.unal.edu.co:unal/310452022-11-29 23:03:52.582Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co
dc.title.spa.fl_str_mv Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
title Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
spellingShingle Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
Informatics
Electrical and Electronic Engineering
Parameter selection
Least Squares-Support Vector Machines
Multidimensional Generalized Cross Validation
Regression.
title_short Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
title_full Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
title_fullStr Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
title_full_unstemmed Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
title_sort Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation
dc.creator.fl_str_mv Álvarez-Meza, Andrés Marino
Daza Santacoloma, Genaro
Acosta Mejia, Carlos
Castallanos Dominguez, German
dc.contributor.author.spa.fl_str_mv Álvarez-Meza, Andrés Marino
Daza Santacoloma, Genaro
Acosta Mejia, Carlos
Castallanos Dominguez, German
dc.subject.proposal.spa.fl_str_mv Informatics
Electrical and Electronic Engineering
Parameter selection
Least Squares-Support Vector Machines
Multidimensional Generalized Cross Validation
Regression.
topic Informatics
Electrical and Electronic Engineering
Parameter selection
Least Squares-Support Vector Machines
Multidimensional Generalized Cross Validation
Regression.
description In this work a new methodology for automatic selection of the free parameters in the Least Squares–Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does not require a prior knowledge about the influence of the LS-SVM free parameters in the results. The methodology is tested on two artificial and two real-world data sets. According to the results our methodology computes suitable regressions with competitive relative errors.
publishDate 2012
dc.date.issued.spa.fl_str_mv 2012
dc.date.accessioned.spa.fl_str_mv 2019-06-26T14:19:28Z
dc.date.available.spa.fl_str_mv 2019-06-26T14:19:28Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/31045
dc.identifier.eprints.spa.fl_str_mv http://bdigital.unal.edu.co/21121/
url https://repositorio.unal.edu.co/handle/unal/31045
http://bdigital.unal.edu.co/21121/
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.spa.fl_str_mv http://revistas.unal.edu.co/index.php/dyna/article/view/17407
dc.relation.ispartof.spa.fl_str_mv Universidad Nacional de Colombia Revistas electrónicas UN Dyna
Dyna
dc.relation.ispartofseries.none.fl_str_mv Dyna; Vol. 79, núm. 171 (2012); 23-30 DYNA; Vol. 79, núm. 171 (2012); 23-30 2346-2183 0012-7353
dc.relation.references.spa.fl_str_mv Álvarez-Meza, Andrés Marino and Daza Santacoloma, Genaro and Acosta Mejia, Carlos and Castallanos Dominguez, German (2012) Parameter selection in least squares-support vector machines regression oriented, using generalized cross-validation. Dyna; Vol. 79, núm. 171 (2012); 23-30 DYNA; Vol. 79, núm. 171 (2012); 23-30 2346-2183 0012-7353 .
dc.rights.spa.fl_str_mv Derechos reservados - Universidad Nacional de Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
Derechos reservados - Universidad Nacional de Colombia
http://creativecommons.org/licenses/by-nc/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia Sede Medellín
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/31045/1/17407-158373-1-PB.html
https://repositorio.unal.edu.co/bitstream/unal/31045/2/17407-106197-1-PB.pdf
https://repositorio.unal.edu.co/bitstream/unal/31045/3/17407-106197-1-PB.pdf.jpg
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bitstream.checksumAlgorithm.fl_str_mv MD5
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repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
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