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

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...

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Detalles Bibliográficos
Autores principales: Granitto, Pablo Miguel, Verdes, Pablo Fabián, Ceccatto, Hermenegildo Alejandro
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2003
Materias:
Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22869
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22869
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
spellingShingle Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
Aggregation algorithms for regression : A comparison with boosting and SVM techniques
topic_facet Ciencias Informáticas
Comparison with Boosting
ARTIFICIAL INTELLIGENCE
Intelligent agents
SVM Techniques
Aggregation Algorithms
Regression
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.
format Objeto de conferencia
Objeto de conferencia
author Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author_facet Granitto, Pablo Miguel
Verdes, Pablo Fabián
Ceccatto, Hermenegildo Alejandro
author_sort Granitto, Pablo Miguel
title Aggregation algorithms for regression : A comparison with boosting and SVM techniques
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
publishDate 2003
url http://sedici.unlp.edu.ar/handle/10915/22869
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