DocumentCode :
2835535
Title :
Target Attribute Identification Based on Multi-Class SVM and D-S Evidence Theory
Author :
Jin, Xu ; Lan Jiangqiao ; Xu Jin
Author_Institution :
Dept. of Early Warning Surveillance Intell., AFRA, Wuhan, China
fYear :
2009
fDate :
11-13 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
Concerning for air target attribute identification, an attribute identification method based on the combination of multi-class SVM and D-S evidence theory is proposed. The method constructs several multi-class support vector machine (SVM) classifiers, and generates the basic probability assignment (BPA) by the class-wise probability. Then D-S evidence theory is adopted to make the fusion and decision. Simulation results indicate that the method have a good identification of air target attribute, and prove the rationality and validity.
Keywords :
pattern classification; probability; sensor fusion; support vector machines; target tracking; DS evidence theory; air target attribute identification; basic probability assignment; class wise probability; multiclass SVM classifier; support vector machine; target attribute identification; Fusion power generation; Fuzzy reasoning; Machine intelligence; Risk management; Statistical learning; Support vector machine classification; Support vector machines; Surveillance; Target recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
Type :
conf
DOI :
10.1109/CISE.2009.5364400
Filename :
5364400
Link To Document :
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