A Comparative Evaluation of Sequential Constructive Methods

摘要

A detailed comparison of the most promising algorithms in the class of sequential constructive methods is performed by employing artificial benchmarks and real-world problems. Four of them incrementally build the hidden layer of the resulting neural network by subsequently adding threshold units. The last method considered makes use of neurons with a window-shaped activation function. The performances obtained with each technique and their comparison with the widely employed back-propagation algorithm allow a thorough examination of the characteristics of these sequential constructive methods. In particular, the neural networks trained show good generalization ability also when the learning time is very low.