| 词条 | Rprop |
| 释义 |
Similarly to the Manhattan update rule, Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight". For each weight, if there was a sign change of the partial derivative of the total error function compared to the last iteration, the update value for that weight is multiplied by a factor η−, where η− < 1. If the last iteration produced the same sign, the update value is multiplied by a factor of η+, where η+ > 1. The update values are calculated for each weight in the above manner, and finally each weight is changed by its own update value, in the opposite direction of that weight's partial derivative, so as to minimise the total error function. η+ is empirically set to 1.2 and η− to 0.5. Next to the cascade correlation algorithm and the Levenberg–Marquardt algorithm, Rprop is one of the fastest weight update mechanisms. RPROP is a batch update algorithm. VariationsMartin Riedmiller developed three algorithms, all named RPROP. Igel and Hüsken assigned names to them and added a new variant:[2] [3]
References1. ^Martin Riedmiller und Heinrich Braun: Rprop - A Fast Adaptive Learning Algorithm. Proceedings of the International Symposium on Computer and Information Science VII, 1992 2. ^1 Christian Igel and Michael Hüsken. Improving the Rprop Learning Algorithm. Second International Symposium on Neural Computation (NC 2000), pp. 115-121, ICSC Academic Press, 2000 3. ^1 2 Christian Igel and Michael Hüsken. Empirical Evaluation of the Improved Rprop Learning Algorithm. Neurocomputing 50:105-123, 2003 4. ^Martin Riedmiller and Heinrich Braun. A direct adaptive method for faster backpropagation learning: The Rprop algorithm. Proceedings of the IEEE International Conference on Neural Networks, 586-591, IEEE Press, 1993 5. ^Martin Riedmiller. Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms. Computer Standards and Interfaces 16(5), 265-278, 1994 6. ^Martin Riedmiller. Rprop – Description and Implementation Details. Technical report, 1994 External links
2 : Artificial neural networks|Machine learning algorithms |
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