Empirical evaluation of the improved Rprop learning algorithms

The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing first-order learning methods for neural networks. We discuss modifications of this algorithm that improve its learning speed. The new optimization methods are empirically compared to the existing Rprop variants, the conjugate gradient method, Quickprop, and the BFGS algorithm on a set of neural network benchmark problems. The improved Rprop outperforms the other methods; only the BFGS performs better in the later stages of learning on some of the test problems. For the analysis of the local search behavior, we compare the Rprop algorithms on general hyperparabolic error landscapes, where the new variants confirm their improvement.