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Optimized Support Vector Machines for Nonstationary Signal Classification

2002

Article

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This letter describes an efficient method to perform nonstationary signal classification. A support vector machine (SVM) algorithm is introduced and its parameters optimised in a principled way. Simulations demonstrate that our low complexity method outperforms state-of-the-art nonstationary signal classification techniques.

Author(s): Davy, M. and Gretton, A. and Doucet, A. and Rayner, PJW.
Journal: IEEE Signal Processing Letters
Volume: 9
Number (issue): 12
Pages: 442-445
Year: 2002
Month: December
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/LSP.2002.806070
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@article{2136,
  title = {Optimized Support Vector Machines for Nonstationary Signal Classification},
  author = {Davy, M. and Gretton, A. and Doucet, A. and Rayner, PJW.},
  journal = {IEEE Signal Processing Letters},
  volume = {9},
  number = {12},
  pages = {442-445},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = dec,
  year = {2002},
  month_numeric = {12}
}