The learning of linear neural nets with anti-Hebbian rules

被引：0
|
作者：
Matsuoka, K
Kawamoto, M
机构：
[1] Faculty of Engineering, Kyushu Institute of Technology, Kitayushu
[2] Kyushu Institute of Technology, Kitakyushu
[3] University of Tokyo, Tokyo
来源：
SYSTEMS AND COMPUTERS IN JAPAN | 1996年 / 27卷 / 03期
关键词：
neural networks; self-organization; learning; anti-Hebbian rules; Hebbian rules;
D O I：
10.1002/scj.4690270308
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Hebbian rule might be the most popular one as an unsupervised learning model of neural nets. Recently, the opposite of the Hebbian rule, i.e., the so-called anti-Hebbian rule, has drawn attention as a new learning paradigm. This paper first clarifies some fundamental properties of the anti-Hebbian rule, and then shows that a variety of networks can be acquired by some anti-Hebbian rules.
引用
收藏
页码：84 / 93
页数：10
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