Improving Bayesian Regularization of ANN via Pre-training with Early-Stopping

摘要

We propose a simple method that enhances the performance of Bayesian Regularization of Artificial Neural Network (ANN) through pre-training of initial network with the Early-Stopping algorithm. The proposed method is applied to the regularization of Feed-forward Neural Networks to regress three benchmark data series. Significant reduction in both the cross-validation error and the number of training over standard Bayesian Regularisation is achieved.