Partially activated neural networks by controlling information

Abstract

In this paper, we propose partial activation to simplify complex neural networks. For choosing important elements in a network, we develop a fully supervised competitive learning that can deal with any targets. This approach is an extension of competitive learning to a more general one, including supervised learning. Because competitive learning focuses on an important competitive unit, all the other competitive units are of no use. Thus, the number of connection weights to be updated can be reduced to a minimum point when we use competitive learning. We apply the method to the XOR problem to show that learning is possible with good interpretability of internal representations. Then, we apply the method to a student survey. In the problem, we try to show that the new method can produce connection weights that are more stable than those produced by BP. In addition, we show that, though connection weights are quite similar to those produced by linear regression analysis, generalization performance can be improved by changing the number of competitive units. © Springer-Verlag Berlin Heidelberg 2007.