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Conference Paper

Nonstationary Signal Classification using Support Vector Machines

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Gretton, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Gretton, A., Davy M, Doucet, A., & Rayner, P. (2001). Nonstationary Signal Classification using Support Vector Machines. In 11th IEEE Workshop on Statistical Signal Processing (pp. 305-305).

Abstract

In this paper, 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 the Cohen's group time-frequency representation, which is used to process the non-stationary signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data.