Abstract
As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance. However there are several methods to construct the ensemble. In this paper we present some new results in a comparison of twenty different methods. We have trained ensembles of 3, 9, 20 and 40 networks to show results in a wide spectrum of values. The results show that the improvement in performance above 9 networks in the ensemble depends on the method but it is usually low. Also, the best method for a ensemble of 3 networks is called “Decorrelated” and uses a penalty term in the usual Backpropagation function to decorrelate the networks outputs in the ensemble. For the case of 9 and 20 networks the best method is conservative boosting. And finally for 40 networks the best method is Cels.
This research was supported by the project MAPACI TIC2002-02273 of CICYT in Spain.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Torres-Sospedra, J., Hernández-Espinosa, C., Fernández-Redondo, M. (2005). New Results on Ensembles of Multilayer Feedforward. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_23
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DOI: https://doi.org/10.1007/11550907_23
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