Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Konferenzbeitrag

Kernel Methods and Their Applications to Signal Processing

MPG-Autoren
/persons/resource/persons83824

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

Externe Ressourcen

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1202779
(Verlagsversion)

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.


Zitierlink: https://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.