Learning and validation in neural network ensembles

Autores
Granitto, Pablo Miguel; Verdes, Pablo Fabián; Ceccatto, Hermenegildo Alejandro; Navone, P. F.
Año de publicación
2001
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Ensembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here a simple method for learning and validation in regression/classification ensembles of ANN that leads to overtrained aggregate members with an adequate balance between accuracy and diversity. The algorithm is favorably tested against other methods recently proposed in the literature, producing an improvement in performance on the standard statistical databases used as benchmarks.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Learning and validation
ARTIFICIAL INTELLIGENCE
neural network ensembles
Learning
Validation
Neural nets
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:10915/21665
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/21665
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Learning and validation in neural network ensemblesGranitto, Pablo MiguelVerdes, Pablo FabiánCeccatto, Hermenegildo AlejandroNavone, P. F.Ciencias InformáticasLearning and validationARTIFICIAL INTELLIGENCEneural network ensemblesLearningValidationNeural netsEnsembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here a simple method for learning and validation in regression/classification ensembles of ANN that leads to overtrained aggregate members with an adequate balance between accuracy and diversity. The algorithm is favorably tested against other methods recently proposed in the literature, producing an improvement in performance on the standard statistical databases used as benchmarks.Eje: Inteligencia Computacional - MetaheurísticasRed de Universidades con Carreras en Informática (RedUNCI)2001-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/21665enginfo: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:UNLP2025-09-03T10:27:32Zoai:sedici.unlp.edu.ar:10915/21665Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:33.209SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Learning and validation in neural network ensembles
title Learning and validation in neural network ensembles
spellingShingle Learning and validation in neural network ensembles
Granitto, Pablo Miguel
Ciencias Informáticas
Learning and validation
ARTIFICIAL INTELLIGENCE
neural network ensembles
Learning
Validation
Neural nets
title_short Learning and validation in neural network ensembles
title_full Learning and validation in neural network ensembles
title_fullStr Learning and validation in neural network ensembles
title_full_unstemmed Learning and validation in neural network ensembles
title_sort Learning and validation in neural network ensembles
dc.creator.none.fl_str_mv Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
Navone, P. F.
author Granitto, Pablo Miguel
author_facet Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
Navone, P. F.
author_role author
author2 Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
Navone, P. F.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Learning and validation
ARTIFICIAL INTELLIGENCE
neural network ensembles
Learning
Validation
Neural nets
topic Ciencias Informáticas
Learning and validation
ARTIFICIAL INTELLIGENCE
neural network ensembles
Learning
Validation
Neural nets
dc.description.none.fl_txt_mv Ensembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here a simple method for learning and validation in regression/classification ensembles of ANN that leads to overtrained aggregate members with an adequate balance between accuracy and diversity. The algorithm is favorably tested against other methods recently proposed in the literature, producing an improvement in performance on the standard statistical databases used as benchmarks.
Eje: Inteligencia Computacional - Metaheurísticas
Red de Universidades con Carreras en Informática (RedUNCI)
description Ensembles of artificial neural networks (ANN) have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. We propose here a simple method for learning and validation in regression/classification ensembles of ANN that leads to overtrained aggregate members with an adequate balance between accuracy and diversity. The algorithm is favorably tested against other methods recently proposed in the literature, producing an improvement in performance on the standard statistical databases used as benchmarks.
publishDate 2001
dc.date.none.fl_str_mv 2001-05
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/10915/21665
url http://sedici.unlp.edu.ar/handle/10915/21665
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.13397

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