Unsupervised Hebbian learning in neural networks

被引：0
|
作者：
Freisleben, B [1 ]
Hagen, C [1 ]
机构：
[1] Univ Gesamthsch Siegen, Dept Elect Engn & Comp Sci, D-57068 Siegen, Germany
来源：
COMPUTING ANTICIPATORY SYSTEMS: CASYS - FIRST INTERNATIONAL CONFERENCE | 1998年 / 437卷
关键词：
unsupervised Hebbian learning; principal component analysis; minor component analysis; independent component analysis;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, a survey of a particular class of unsupervised learning rules for neural networks is presented. These learning rules are based on variants of Hebbian correlation learning to update the connection weights of two-layer network architectures consisting of an input layer with n units and an output layer with m units. It will be demonstrated that the networks are able to perform a variety of important data analysis tasks, including Principal Component Analysis (PCA), Minor Component Analysis (MCA) and Independent Component Analysis (ICA).
引用
收藏
页码：606 / 625
页数：20
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