A new Meta Machine Learning (MML) method based on combining non-significant different neural networks
Andrés Yáñez Escolano, J. Pizarro, Elisa Guerrero, Pedro L. Galindo · The European Symposium on Artificial Neural Networks · 2003
Model combination provides an alternative to model selection. With a little additional effort we can obtain MML models that improve the generalization capabilities of their individual members. However, it has been recognized that the individual members must be as accurate and diverse as possible. In this paper we present a novel method for building MML models by combining neural networks which are not significantly different from the network selected by some model selection method.