Constructive and pruning methods for neural network design

摘要

This paper presents methods to improve generalization of Multi-Layer Perceptron (MLP) by pruning the original topology without loss in performance. Topology information and validation sets are used. The results show that these techniques are able to choose a minimum network topology and to simplify trained networks.