New Algorithm for Building Effective Model from Prediction Models Using Parallel Data

Abstract

Building much more effective new hybrid models from prediction models, using parallel data is discussed. The algorithm for selection of model pairs and its advantage over any best prediction model is provided. The advantage of prediction models with higher number of pairs over lower number of pairs is shown and the algorithm of taking into consideration the “approximate coincidence” of predictions is discussed when selecting pairs.