Title :
Optimized support vector machines for nonstationary signal classification
Author :
Davy, Manuel ; Gretton, Arthur ; Doucet, Arnaud ; Rayner, Peter J W
Author_Institution :
Inst. de Recherche en Commun. et Cybernetique de Nantes, France
Abstract :
This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimized in a principled way. Simulations demonstrate that our low-complexity method outperforms state-of-the-art nonstationary signal classification techniques.
Keywords :
computational complexity; learning automata; signal classification; SVM algorithm; low-complexity method; nonstationary signal; nonstationary signal classification; optimized support vector machines; Acoustic measurements; Associate members; Classification algorithms; Frequency domain analysis; Kernel; Pattern classification; Support vector machine classification; Support vector machines; Time frequency analysis;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2002.806070