多层感知器的局部监督学习算法

D. S. Vlachos
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引用次数: 0

摘要

多层感知器在进行监督训练时,误差的反向传播是一种空间上的非局部算法,即需要了解网络拓扑结构。另一方面,具有许多隐藏单元的生物系统中的学习规则似乎在空间和时间上都是局部的。本文提出了一种不区分输入层、隐藏层和输出层的局部学习算法。给出了仿真结果,并与其他知名训练算法进行了比较。(©2004 WILEY-VCH Verlag GmbH &KGaA公司,Weinheim)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Local Supervised Learning Algorithm For Multi-Layer Perceptrons

The back propagation of error in multi-layer perceptrons when used for supervised training is a non-local algorithm in space, that is it needs the knowledge of the network topology. On the other hand, learning rules in biological systems with many hidden units, seem to be local in both space and time. In this work, a local learning algorithm is proposed which makes no distinction between input, hidden and output layers. Simulation results are presented and compared with other well known training algorithms. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

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