Nonstationary signal classification using support vector machines
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.