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P. M. Granitto ; P. F. Verdes ; H. A. Ceccatto ;Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial 2001, 5 (12)

Author: H. D. Navone

Source: http://www.redalyc.org/


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Inteligencia Artificial.
Revista Iberoamericana de Inteligencia Artificial ISSN: 1137-3601 revista@aepia.org Asociación Española para la Inteligencia Artificial España Navone, H.
D.; Granitto, P.
M.; Verdes, P.
F.; Ceccatto, H.
A. A learning algorithm for neural network ensembles Inteligencia Artificial.
Revista Iberoamericana de Inteligencia Artificial, vol.
5, núm.
12, primavera, 2001, pp.
70-74 Asociación Española para la Inteligencia Artificial Valencia, España Available in: http:--www.redalyc.org-articulo.oa?id=92551209 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative A Learning Algorithm For Neural Network Ensembles H.
D.
Navone, P.
M.
Granitto, P.
F.
Verdes and H.
A.
Ceccatto Instituto de Física Rosario (CONICET-UNR) Blvd.
27 de Febrero 210 Bis, 2000 Rosario.
República Argentina. {navone,verdes,granitto,ceccatto}@ifir.edu.ar Abstract The performance of a single regressor-classifier can be improved by combining the outputs of several predictors.
This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabilities.
We propose here a new method for selecting members of regression-classification ensembles.
In particular, using artificial neural networks as learners in a regression context, we show that this method leads to small aggregates with few but very diverse individual networks.
The algorithm is favorably tested against other methods recently proposed in the literature, producing equal performance on the standard statistical databases used as benchmarks with ensembles that have 75% less members on average. Keywords: Neural Networks, Ensemble Learning, Regression, Bias-Variance Decompos...





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