Improving the Rprop Learning Algorithm 论文

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Neural Networks and ApplicationsModel Reduction and Neural NetworksMachine Learning and Data Classification
人工智能Neural Networks and ApplicationsMachine Learning and Data ClassificationModel Reduction and Neural Networks
相关技术:Neural Networks and ApplicationsMachine Learning and Data ClassificationModel Reduction and Neural Networks
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摘要

The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing first-order learning methods for neural networks. We introduce modifications of the algorithm that improve its learning speed. The resulting speedup is experimentally shown for a set of neural network learning tasks as well as for artificial error surfaces.

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Michael Hüsken

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Christian Igel

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Neural Networks and ApplicationsMachine Learning and Data ClassificationModel Reduction and Neural Networks

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