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- Rprop abstract "Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992.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.".
- Rprop wikiPageExternalLink summary?doi=10.1.1.17.1332.
- Rprop wikiPageExternalLink summary?doi=10.1.1.21.1417.
- Rprop wikiPageExternalLink summary?doi=10.1.1.21.3428.
- Rprop wikiPageExternalLink summary?doi=10.1.1.27.7876.
- Rprop wikiPageExternalLink RpropToolbox.
- Rprop wikiPageExternalLink 32445-rprop.
- Rprop wikiPageID "7950358".
- Rprop wikiPageRevisionID "582496004".
- Rprop hasPhotoCollection Rprop.
- Rprop subject Category:Machine_learning_algorithms.
- Rprop subject Category:Neural_networks.
- Rprop type Abstraction100002137.
- Rprop type Communication100033020.
- Rprop type ComputerArchitecture106725249.
- Rprop type Description106724763.
- Rprop type Message106598915.
- Rprop type NeuralNetwork106725467.
- Rprop type NeuralNetworks.
- Rprop type Specification106725067.
- Rprop type Statement106722453.
- Rprop comment "Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992.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".".
- Rprop label "Resilient Propagation".
- Rprop label "Rprop".
- Rprop sameAs Resilient_Propagation.
- Rprop sameAs m.026l567.
- Rprop sameAs Q1320470.
- Rprop sameAs Q1320470.
- Rprop sameAs Rprop.
- Rprop wasDerivedFrom Rprop?oldid=582496004.
- Rprop isPrimaryTopicOf Rprop.