Noise derived information criterion for model selection

Abstract.. This paper proposes a new complexity-penalization model selection strategy derived from the minimum risk principle and the behavior of candidate models under noisy conditions. This strategy seems to be robust in small sample size conditions and tends to AIC criterion as sample size grows up. The simulation study at the end of the paper will show that the proposed criterion is extremely competitive when compared to other state-of-the-art criteria. Keywords: Model Selection. Minimum Prediction Risk. Network Size. 1