Nonstationary Signal Classification using Support Vector Machines
2001
Conference Paper
ei
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.
Author(s): | Gretton, A. and Davy, M. and Doucet, A. and Rayner, PJW. |
Journal: | 11th IEEE Workshop on Statistical Signal Processing |
Pages: | 305-305 |
Year: | 2001 |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | 11th IEEE Workshop on Statistical Signal Processing |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PostScript
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BibTex @inproceedings{2135, title = {Nonstationary Signal Classification using Support Vector Machines}, author = {Gretton, A. and Davy, M. and Doucet, A. and Rayner, PJW.}, journal = {11th IEEE Workshop on Statistical Signal Processing}, pages = {305-305}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, year = {2001}, doi = {} } |