A Near Linear Algorithm for Testing Linear Separability in Two Dimensions

被引：0

作者：

Contassot-Vivier, Sylvain ^{[1
]}

Elizondo, David ^{[2
]}

机构：

[1] Univ Lorraine, UMR 7503, Nancy, France

[2] De Montfort Univ, Ctr Computat Intelligence, Leicester, Leics, England

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS | 2012年 / 311卷

关键词：

Classification; linear separability; 2D geometry;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a near linear algorithm for determining the linear separability of two sets of points in a two-dimensional space. That algorithm does not only detects the linear separability but also computes separation information. When the sets are linearly separable, the algorithm provides a description of a separation hyperplane. For non linearly separable cases, the algorithm indicates a negative answer and provides a hyperplane of partial separation that could be useful in the building of some classification systems.

引用

页码：114 / +

页数：3

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