Online kernel-based classification by projections

Konstantinos Slavakis, Sergios Theodoridis, Isao Yamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The goal of this paper is the development of a novel efficient online kernel-based algorithm for classification. The spirit of the algorithm stems from the recently introduced Adaptive Projected Subgradient Method. This is a general convex analytic tool that employs projections onto a sequence of convex sets and it can be considered as a generalization of the celebrated APA algorithm, widely used in classical adaptive filtering.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Adaptive systems
  • Pattern classification

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