Supervised learning with artificial selection

International 1989 Joint Conference on Neural Networks Pub Date : 1989-12-01 DOI:10.1109/IJCNN.1989.118443

M. Hagiwara, M. Nakagawa

引用次数: 8

Abstract

Summary form only given, as follows. Supervised learning with artificial selection is proposed as a way to escape from local minima. The concept of artificial selection is reasonable for nature. In the authors' method, the 'worst' hidden unit is detected, and then all the weights connected to the detected hidden unit are reset to small random values. According to simulations, only half the trials using conventional backpropagation converge, whereas all of the trials using the proposed method converge, and quickly do so.<>

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人工选择的监督学习

仅给出摘要形式，如下。提出了一种利用人工选择进行监督学习的方法来避免局部最小值问题。人工选择的概念对自然界来说是合理的。在作者的方法中，检测“最差”隐藏单元，然后将与检测到的隐藏单元相连的所有权重重置为小的随机值。根据模拟，使用传统反向传播方法的试验中只有一半收敛，而使用该方法的所有试验都收敛，而且速度很快。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International 1989 Joint Conference on Neural Networks

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