Nonstationary signal classification using support vector machines

Author

Gretton, Arthur ; Davy, Manuel ; Doucet, Arnaud ; Rayner, Peter J W

Author_Institution

Dept. of Eng., Cambridge Univ., UK

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

305

Lastpage

308

Abstract

We demonstrate the use of support vector (SV) techniques for the binary classification of nonstationary sinusoidal signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce Cohen´s group time-frequency representation, which is used to process the nonstationary signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data

Keywords

learning (artificial intelligence); learning automata; signal classification; statistical analysis; time-frequency analysis; Cohen group time-frequency representation; binary classification; nonstationary signal classification; quadratic phase; sinusoidal signals; statistical learning theory; support vector machines; Classification algorithms; Cost function; Frequency domain analysis; Pattern classification; Signal processing; Signal processing algorithms; Statistical learning; Support vector machine classification; Support vector machines; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955283

Filename

955283