Optimized Support Vector Machines for Nonstationary Signal Classification
2002
Article
ei
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 |
Links: |
PostScript
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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}, doi = {10.1109/LSP.2002.806070}, month_numeric = {12} } |