ＴＡＭネットワークのプルーニング手法の提案

Abstract

The TAM (Topographic Attentive Mapping) Network based on a biologically-motivated neural network model is an especially effective model. When the network makes an incorrect output prediction, the attentional feedback circuit modulates the learning rates and adds a node to the category layer in order to improve the network's prediction accuracy. In this paper, a pruning algorithm for reducing links and nodes at the layers is proposed. The usefulness of the algorithm is also illustrated.