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

页码：606 / 625

页数：20

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