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Time-frequency learning machines

Abstract : Over the last decade, the theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces. Our paper is a further contribution which extends the framework of the so-called kernel learning machines to time-frequency analysis, showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.
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Contributor : Patrick Flandrin Connect in order to contact the contributor
Submitted on : Thursday, December 7, 2006 - 9:34:22 AM
Last modification on : Sunday, June 26, 2022 - 4:34:57 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 11:59:21 PM


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  • HAL Id : ensl-00118896, version 1


Paul Honeiné, Cédric Richard, Patrick Flandrin. Time-frequency learning machines. 2006. ⟨ensl-00118896⟩



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