  The cascade training technique which was developed during our work on the
MiniBooNE particle identification has been found to be a very efficient way to
improve the selection performance, especially when very low background
contamination levels are desired. The detailed description of this technique is
presented here based on the MiniBooNE detector Monte Carlo simulations, using
both artifical neural networks and boosted decision trees as examples.
