Improvement in the learning process as a function of distribution characteristics of binary data set

Abstract

In literature improvements in neural learning are reported on, which have been achieved through input data manipulation, based on entirely experimental studies. Theoretical background is not supplied for these studies and neural networks are employed as a 'black box' model. Within this work, this problem is highlighted and the impact of the modified training sets is evaluated in order to establish a theoretical background for the phenomenon. For this end, a number of binary training data is employed to show how does the learning process depend on data distribution within the training sets.