抄録
When we apply a hierarchical neural network based on the back-propagation algorithm to a particular problem, we must determine beforehand the suitable size of network for the problem. But it is a very difficult problem. Too small a network will not learn at all, while too large a network will be inefficient and worsen its generalization ability due to overfitting.
In order to solve this problem, in this paper we propose a compact structuring method based on learning with a large size network and then compacting gradually the network by eliminating extra hidden layers and units. The result is a small and efficient network that performs better than the original. Also we demonstrate the effectiveness of this method by appling it to an identification problem of logic function.
