A Brief Overview on Parameter Optimization of Support Vector Machine

被引：0

作者：

Cao, Qi ^{[1
]}

Yu, Lei ^{[2
]}

Cheng, Mingsheng ^{[3
]}

机构：

[1] Logist Engn Univ, Training Dept, Chongqing 401311, Peoples R China

[2] Chongqing Normal Univ, Coll Comp & Informat Sci, Chongqing 401331, Peoples R China

[3] Naval Univ Engn, Logist Coll, Tianjin 300450, Peoples R China

来源：

2016 3RD INTERNATIONAL CONFERENCE ON SMART MATERIALS AND NANOTECHNOLOGY IN ENGINEERING (SMNE 2016) | 2016年

关键词：

Support vector machine; Parameter selection; Optimization algorithm; Fitness function; CLASSIFICATION; ALGORITHM;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Support vector machine (SVM) has been successfully applied in classification and regression problems. But it is very sensitive to the selection of parameters. The fundamental principles of SVM are analyzed firstly. The main optimization methods and achievements for SVM parameters are introduced. And the popular fitness functions used for the parameter optimization of SVM are described. The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.

引用

页码：275 / 279

页数：5

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