In this report we describe a novel technique to generate a committee architecture for time series prediction. The algorithm, here named Selective Multiple Prediction Network, consists of three steps: a systematic partition of the input hyperspace, a selective training of many agents and a flexible combining strategy. Potencially uncorrelated agents are generated which improves the combination process. The proposed architecture is easily extended to the class of classification problems.
Committe Architecture, Team Prediction, Combining Predictions
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