Kernel Methods and Their Applications to Signal Processing

Bousquet, O; Perez-Cruz, F

doi:10.1109/ICASSP.2003.1202779

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Kernel Methods and Their Applications to Signal Processing

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

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Citation

Bousquet, O., & Perez-Cruz, F. (2003). Kernel Methods and Their Applications to Signal Processing. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03) (pp. 860-863). Piscataway, NJ, USA: IEEE. doi:10.1109/ICASSP.2003.1202779.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DDA8-8

Abstract

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.