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Self-organizing feature map with improved performance by non-monotonic variation of the learning rate
Self-organizing feature map with improved performance by non-monotonic variation of the learning rate
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机译:通过非单调变化学习率来提高性能的自组织特征图
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摘要
The learning rate used for updating the weights of a self-ordering feature map is determined by a process that injects some type of perturbation into the value so that it is not simply monotonically decreased with each training epoch. For example, the learning rate may be generated according to a pseudorandom process. The result is faster convergence of the synaptic weights.
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