Experimental analysis of input weight freezing in constructive neural networks

An important research problem in constructive network algorithms is how to train the new network after the addition of a hidden unit. Some previous empirical analyses performed on the cascade-correlation architecture indicate that the effectiveness of freezing is different for different problem domains and hence is not conclusive. A series of experiments with the single-hidden-layer network on a number of artificial pattern classification problems is described. The performance of the network is compared with and without input weight freezing, and against standard backpropagation. Drawbacks with freezing are identified, and some directions for future work are discussed