Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher
Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher
复制标题
带有噪声教师的感知器在线学习算法的非单调收敛
DOI：
10.1016/j.neunet.2018.02.009
复制
发表时间：
2018
期刊：
影响因子：
7.8
通讯作者：
Miyoshi Seiji
中科院分区：
文献类型：
--
作者：
Ikeda Kazushi;Honda Arata;Hanzawa Hiroaki;Miyoshi Seiji
这里导出了简单感知器的学习曲线。 The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In this paper, we showed how this phenomenon occurs by analyzing the asymptotic property of the perceptron learning using a method in systems science, that is, calculating the eigenvalues of the system matrix and the corresponding eigenvectors. We also analyzed the AdaTron learning and the Hebbian learning in the same way and found that the learning curve of the AdaTron learning is non-monotonic whereas that of the Hebbian learning is monotonic.
Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In this paper, we showed how this phenomenon occurs by analyzing the asymptotic property of the perceptron learning using a method in systems science, that is, calculating the eigenvalues of the system matrix and the corresponding eigenvectors. We also analyzed the AdaTron learning and the Hebbian learning in the same way and found that the learning curve of the AdaTron learning is non-monotonic whereas that of the Hebbian learning is monotonic.