Target Recognition Method Based on Multi-class SVM and Evidence Theory

被引：0

作者：

Quan, Wen ^{[1
]}

Wang, Jian ^{[2
]}

Lei, Lei ^{[2
]}

Gao, Maolin ^{[1
]}

机构：

[1] Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China

[2] Air Force Engn Univ, Air & Missile Def Coll, Xian 710051, Shaanxi, Peoples R China

来源：

ADVANCES IN INTERNETWORKING, DATA & WEB TECHNOLOGIES, EIDWT-2017 | 2018年 / 6卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1007/978-3-319-59463-7_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to conquer the hard outputs defect of Support Vector Machine (SVM) and extend its application, an improved target recognition method based on Multi-class Support Vector Machine (MSVM) is proposed. Firstly, the typical Probability Modeling methodologies of MSVM were deeply analyzed. Secondly, the structure of one-against-one multi-class method which matches with Basic Probability Assignment (BPA) outputs of evidence theory by coincide, so a special Multi-class BPA output method is derived, and multi-sensor target recognition model based on MSVM and two-layer evidence theory is constructed. Finally, the results of experiments show that the proposed approach can not only conquer the overlap area of one-against-one multi-class method, but also improve classification accuracy.

引用

页码：262 / 272

页数：11

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