Study on Supply Chain Partner Selection Based on Support Vector Machine

被引：1

作者：

Li, Wenbo ^{[1
]}

机构：

[1] Zhejiang Normal Univ, Econ & Management Sch, Jinhua 321004, Zhejiang, Peoples R China

来源：

FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6 | 2012年 / 121-126卷

关键词：

Support Vector Machine; Kernel Function; Supply Chain; Partner Selection;

D O I：

10.4028/www.scientific.net/AMM.121-126.4779

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

SVM is a novel machine learning technique developed on empirical risk minimization principle. SVM has many advantages in solving small sample size, nonlinear and high dimensional pattern recognition problem. Based on the study of SVM, this paper discusses its application in the supply chain partner selection that provides a reference for enterprise to select the partner.

引用

页码：4779 / 4783

页数：5

共 8 条

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