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Konferenzbeitrag

Kernel Methods and Their Applications to Signal Processing

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83824

Bousquet,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Bousquet, O. (2003). Kernel Methods and Their Applications to Signal Processing. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03), 860.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DDA8-8
Zusammenfassung
Recently introduced in Machine Learning, the notion of kernels has drawn a lot of interest as it allows to obtain non-linear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the Support Vector Machines has produced significant progress. The successes of such algorithms is now spreading as they are applied to more and more domains. Many Signal Processing problems, by their non-linear and high-dimensional nature may benefit from such techniques. We give an overview of kernel methods and their recent applications.