A bayesian framework for regularized SVM parameter estimation

Jens Gregor, Zhenqiu Liu

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

2 Scopus citations

Abstract

The support vector machine (SVM) is considered here in the context of pattern classification. The emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of the learning samples that we call the support set and then determines not only the weights of the classifier but also the hyperparameter that controls the influence of the regularizing penalty term on basis thereof. We provide numerical results using several data sets from the public domain.

Original languageEnglish (US)
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
EditorsR. Rastogi, K. Morik, M. Bramer, X. Wu
Pages99-105
Number of pages7
DOIs
StatePublished - Dec 1 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
CountryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Gregor, J., & Liu, Z. (2004). A bayesian framework for regularized SVM parameter estimation. In R. Rastogi, K. Morik, M. Bramer, & X. Wu (Eds.), Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 (pp. 99-105). (Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004). https://doi.org/10.1109/ICDM.2004.10094