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Nonstationary Signal Classification using Support Vector Machines

2001

Conference Paper

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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

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}
}