Aggregation algorithms for regression : A comparison with boosting and SVM techniques

Autores
Granitto, Pablo Miguel; Verdes, Pablo Fabián; Ceccatto, Hermenegildo Alejandro
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Classi cation and regression ensembles sho w generalization capabilities that outperform those of single predictors. We present here a further ev aluation of tw o algorithms for ensemble construction recently proposed by us. In particular, we compare them with Boosting and Support Vector Machine tec hniques, which are the newest and most sophisticated methods to treat classi cation and regression problems. We sho w that our comparatively simpler algorithms are very competitive with these tec hniques, showing even a sensible improvement in performance in some of the standard statistical databases used as benchmarks.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22869

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spelling Aggregation algorithms for regression : A comparison with boosting and SVM techniquesGranitto, Pablo MiguelVerdes, Pablo FabiánCeccatto, Hermenegildo AlejandroCiencias InformáticasComparison with BoostingARTIFICIAL INTELLIGENCEIntelligent agentsSVM TechniquesAggregation AlgorithmsRegressionClassi cation and regression ensembles sho w generalization capabilities that outperform those of single predictors. We present here a further ev aluation of tw o algorithms for ensemble construction recently proposed by us. In particular, we compare them with Boosting and Support Vector Machine tec hniques, which are the newest and most sophisticated methods to treat classi cation and regression problems. We sho w that our comparatively simpler algorithms are very competitive with these tec hniques, showing even a sensible improvement in performance in some of the standard statistical databases used as benchmarks.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf485-496http://sedici.unlp.edu.ar/handle/10915/22869enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T10:47:51Zoai:sedici.unlp.edu.ar:10915/22869Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:51.678SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Aggregation algorithms for regression : A comparison with boosting and SVM techniques
title Aggregation algorithms for regression : A comparison with boosting and SVM techniques
spellingShingle Aggregation algorithms for regression : A comparison with boosting and SVM techniques
Granitto, Pablo Miguel
Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
title_short Aggregation algorithms for regression : A comparison with boosting and SVM techniques
title_full Aggregation algorithms for regression : A comparison with boosting and SVM techniques
title_fullStr Aggregation algorithms for regression : A comparison with boosting and SVM techniques
title_full_unstemmed Aggregation algorithms for regression : A comparison with boosting and SVM techniques
title_sort Aggregation algorithms for regression : A comparison with boosting and SVM techniques
dc.creator.none.fl_str_mv Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author Granitto, Pablo Miguel
author_facet Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author_role author
author2 Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
topic Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
dc.description.none.fl_txt_mv Classi cation and regression ensembles sho w generalization capabilities that outperform those of single predictors. We present here a further ev aluation of tw o algorithms for ensemble construction recently proposed by us. In particular, we compare them with Boosting and Support Vector Machine tec hniques, which are the newest and most sophisticated methods to treat classi cation and regression problems. We sho w that our comparatively simpler algorithms are very competitive with these tec hniques, showing even a sensible improvement in performance in some of the standard statistical databases used as benchmarks.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Classi cation and regression ensembles sho w generalization capabilities that outperform those of single predictors. We present here a further ev aluation of tw o algorithms for ensemble construction recently proposed by us. In particular, we compare them with Boosting and Support Vector Machine tec hniques, which are the newest and most sophisticated methods to treat classi cation and regression problems. We sho w that our comparatively simpler algorithms are very competitive with these tec hniques, showing even a sensible improvement in performance in some of the standard statistical databases used as benchmarks.
publishDate 2003
dc.date.none.fl_str_mv 2003-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22869
url http://sedici.unlp.edu.ar/handle/10915/22869
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
485-496
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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