THE CLASSIFICATION CAPABILITY OF A DYNAMIC THRESHOLD NEURAL-NETWORK

Abstract

This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN). Through theoretical analysis, we prove that the classification capability of a DTNN can be twice as effective as a conventional sigmoidal multilayer neural network in classification capability. In other words, to successfully learn an arbitrarily given training set, a DTNN may need as little as half the number of free parameters required by a sigmoidal multilayer neural network.