Abstract. Neural network ensembles are applied to handwritten digit recognition. The invidual networks of the ensemble are combinations of sparse Look-Up Tables with random receptive fields. It is shown that the consensus of a group of networks outperform the best invidual of the ensemble and further we show that it is possible to estimate the ensemble performance as well as the learning curve, on a medium size database. In addition we present preliminary analysis of experiments on a large data base and show that state o f t h e ad performance can be obtained using the ensemble approach by optimizing the receptive fields.
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