In this paper we propose an algorithm we call "NeuralBAG" that estimates the set of weights and number of hidden units each network in a bagged ensemble should have so that the generalization performance of the ensemble is optimized. Experiments performed on noisy synthetic data demonstrate the potential of the algorithm. On average, ensembles trained using NeuralBAG out-perform bagged networks trained using cross-validation by 53% and individual networks trained using "cheating" by 32%.
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