A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm (1993)
| Venue: | IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS |
| Citations: | 505 - 32 self |
BibTeX
@INPROCEEDINGS{Riedmiller93adirect,
author = {Martin Riedmiller and Heinrich Braun},
title = {A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm},
booktitle = {IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS},
year = {1993},
pages = {586--591},
publisher = {}
}
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Abstract
A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforseeable influence of the size of the derivative but only dependent on the temporal behaviour of its sign. This leads to an efficient and transparent adaptation process. The promising capabilities of RPROP are shown in comparison to other wellknown adaptive techniques.
Citations
| 283 | Increased rates of convergence through learning rate adaptation - Jacobs |
| 205 | An Empirical Study of Learning Speed in Back-Propagation Networks - Fahlman - 1988 |
| 118 | Learning to tell two spirals apart - Lang, Witbrock - 1988 |
| 55 | Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons - Schiffmann, Joost, et al. - 1993 |
| 49 | Supersab: fast adaptive backpropagation with good scaling properties - tollenaere - 1990 |
| 8 | Learning strategies for solving the problem of planning using backpropagation - Braun, Feulner, et al. - 1991 |
