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Fast and Stable Learning Utilizing Singular Regions of Multilayer Perceptron

机译:利用多层感知器的奇异区域快速稳定地学习

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摘要

In the parameter space of MLP(J), multilayer perceptron with J hidden units, there exist flat areas called singular regions created by applying reducibility mappings to the optimal solution of MLP(7 - 1). Since such singular regions cause serious stagnation of learning, a learning method to avoid singular regions has been desired. However, such avoiding does not guarantee the quality of the final solutions. This paper proposes a new learning method which does not avoid but makes good use of singular regions to stably and successively find excellent solutions commensurate with MLP(J). The proposed method worked well in our experiments using artificial and real data sets.
机译:在具有J隐藏单元的多层感知器MLP(J)的参数空间中,存在通过将可约性映射应用于MLP(7-1)的最优解而创建的称为奇异区域的平坦区域。由于这样的奇异区域导致学习的严重停滞,因此期望一种避免奇异区域的学习方法。但是,这种避免不能保证最终解决方案的质量。本文提出了一种新的学习方法,该方法不能避免,但要充分利用奇异区域来稳定,连续地找到与MLP相称的优良解(J)。所提出的方法在使用人工和真实数据集的实验中效果很好。

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