Repeated potentiality assimilation: Simplifying learning procedures by positive, independent and indirect operation for improving generalization and interpretation

The present paper aims to present a new computational method called “repeated assimilation” to enhance the potential learning. The potential learning has been developed to simplify the information maximization methods. The method is based on indirect information maximization and the positive firing of neurons with the independent operation of error minimization and potentiality control. The method is simple enough to be applied to many problems. However, it was observed that the information content was not sufficiently increased by the method in particular for the complex problems. To solve this problem, we here propose a computational method in which potentiality application procedures are successively applied until information reaches the sufficiently large values. The method was applied to the well-known Australian credit data set from the machine learning database. In the experiment, it was found that the number of epochs was sufficient small to reach the final state of learning. Information, in particular, information content in hidden neurons increased to sufficiently large values. Then, it was observed that generalization errors decreased in proportion to increase in information. Finally, compared with other conventional methods, it was confirmed that the best generalization performance in any measures could be obtained by the present method.