Header logo is

Kernel Methods and Their Applications to Signal Processing

2003

Conference Paper

ei


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.

Author(s): Bousquet, O. and Perez-Cruz, F.
Book Title: Proceedings. (ICASSP ‘03)
Journal: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03)
Volume: Special Session on Kernel Methods
Pages: 860
Year: 2003
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICASSP 2003

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
PostScript

BibTex

@inproceedings{2018,
  title = {Kernel Methods and Their Applications to Signal Processing},
  author = {Bousquet, O. and Perez-Cruz, F.},
  journal = {IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ‘03)},
  booktitle = {Proceedings. (ICASSP ‘03)},
  volume = {Special Session on Kernel Methods},
  pages = {860 },
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2003}
}