Adaptive regularization in neural network modeling

In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme i s an extended version of the recently presented algorithm [25]. The idea is to minimize an empirical estimate - like the cross-validation estimate - o f the generalization error with respect to regularization parameters. This is done by employing a simple iterative gradient descent scheme using virtu ally no additional programming overhead compared to standard training. Expe riments with feed-forward neural network models for time series prediction and classification tasks showed the viability and robustness of the algorit hm. Moreover, we provided some simple theoretical examples in order to illu strate the potential and limitations of the proposed regularization framewo rk.