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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22869
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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 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
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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