Margin-like quantities and generalized approximate cross validation for support vector machines

Grace Wahba, Yi Lin, Hao Zhang

Research output: Contribution to conferencePaper

8 Scopus citations

Abstract

We examine Support Vector Machines from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the Generalized Comparative Kullback-Leibler Distance as a target for choosing tuning parameters in SVM's, and we propose that the Generalized Approximate Cross Validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the GACV and the SVM margin.

Original languageEnglish (US)
Pages12-20
Number of pages9
StatePublished - Dec 1 1999
Externally publishedYes
EventProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
Duration: Aug 23 1999Aug 25 1999

Conference

ConferenceProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
CityMadison, WI, USA
Period8/23/998/25/99

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Wahba, G., Lin, Y., & Zhang, H. (1999). Margin-like quantities and generalized approximate cross validation for support vector machines. 12-20. Paper presented at Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99), Madison, WI, USA, .