Balanced Resampling for Neural Model Selection

In this paper we apply the balanced resampling, which is an efficient bootstrap technique for neural model selection. Our goal is to reduce computer time, so that in the bootstrap procedure, resampling is not done uniformly, this distribution is modified to obtain variance reduction. Efficiency property of this alternative distribution is shown, together with numerical data.