Target recognition based on a dynamic SVM

Author

Da Lianglong ; Shi Guangzhi ; Hu Junchuan ; Pang Xiaonan

Author_Institution

Navy Submarine Acad., Qingdao, China

fYear

2010

fDate

26-28 June 2010

Firstpage

2488

Lastpage

2491

Abstract

A dynamic support vector machine method is applied to the target recognition using noise power spectrum. It searches the optimal separating hyperplane of the local space taking the target feature as center. To show better importance of each sample to the target feature, the penalty function is measured by using the distance between the target feature and each training sample.

Keywords

object recognition; support vector machines; dynamic support vector machine; noise power spectrum; optimal separating hyperplane; penalty function; target recognition; Constraint optimization; Machine learning; Quadratic programming; Risk management; Sea measurements; Statistical learning; Support vector machine classification; Support vector machines; Target recognition; Underwater vehicles; Dynamic Support vector machine (DSVM); Penalty function; Target recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechanic Automation and Control Engineering (MACE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-7737-1

Type

conf

DOI

10.1109/MACE.2010.5535551

Filename

5535551

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3348307