On weight initialization in cascade-correlation learning
Mikko I. Lehtokangas · 2003
The so called candidate training is commonly used to deal with the initialization problem in the cascade-correlation learning. There several candidate hidden units are first trained, and then the one yielding the best value for the covariance criterion is installed in the network. In the case where there are many candidate units to be trained, the total computational cost of the training can become very large. Here we consider an approach for weight initialization in the cascade-correlation learning. The proposed method is based on the concept of stepwise regression. Empirical simulations demonstrate that the proposed method can substantially speed-up the cascade-correlation learning compared to the case where the candidate training is used. Moreover the overall performance remained the same or was even better than with the candidate training.