Short Outburst Radiator Classification Based on LS-SVM

Author

Xiaoying, Fang ; Xiaoyi, Zhang ; Jia, Yuan

Author_Institution

Dept. of Commun. Eng., Zhengzhou Inf. Sci. & Technol. Inst., Zhengzhou, China

Volume

2

fYear

2009

fDate

25-26 July 2009

Firstpage

531

Lastpage

534

Abstract

A short outburst radiator classification algorithm based on LS-SVM (Least-Square Support Vector Machine) is presented in this paper. The theory of feature extracting and LS-SVM are introduced. And then, the classifier used in short outburst radiator is designed with cross-validation and grid-search to obtain training parameters. A multi-classifier with three coding schemes is also designed. Compared with C-SVM, the simulations show that the performance of LS-SVM is better than C-SVM, and the accurate recognition rate is above 90%, which demonstrates that the classifier is feasible and effective.

Keywords

least squares approximations; support vector machines; coding schemes; feature extraction; grid-search; least-square support vector machine; short outburst radiator classification; Cities and towns; Clustering algorithms; Data mining; Feature extraction; Frequency; Information science; Signal design; Support vector machine classification; Support vector machines; Testing; Cross-validation; Grid-search; LS-SVM; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Computer Science, 2009. ITCS 2009. International Conference on

Conference_Location

Kiev

Print_ISBN

978-0-7695-3688-0

Type

conf

DOI

10.1109/ITCS.2009.244

Filename

5190295