An Expanded Training Set Based Validation Method to Avoid Overfitting for Neural Network Classifier
Kai Wang, Jufeng Yang, Guangshun Shi, Qingren Wang · 2008
The overfitting is a problem of fundamental significance with great implications in the applications of neural network. To avoid overfitting, cross-validation has been proposed. However, in many cases the training set is too small so that cross-validation cannot be applied. Aiming at the problem, a new validation method based on expanded training sets is proposed in this paper. Experimental results show that the generalization ability of neural networks can be greatly improved by the proposed validation method.