A bayesian framework for regularized SVM parameter estimation

Jens Gregor; Zhenqiu Liu

doi:10.1109/ICDM.2004.10094

A bayesian framework for regularized SVM parameter estimation

Jens Gregor, Zhenqiu Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Editors	R. Rastogi, K. Morik, M. Bramer, X. Wu
Pages	99-105
Number of pages	7
DOIs	https://doi.org/10.1109/ICDM.2004.10094
State	Published - 2004
Event	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom Duration: Nov 1 2004 → Nov 4 2004

Publication series

Name	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

Other	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/Territory	United Kingdom
City	Brighton
Period	11/1/04 → 11/4/04

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ICDM.2004.10094

Cite this

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title = "A bayesian framework for regularized SVM parameter estimation",

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.",

author = "Jens Gregor and Zhenqiu Liu",

year = "2004",

doi = "10.1109/ICDM.2004.10094",

language = "English (US)",

isbn = "0769521428",

series = "Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004",

pages = "99--105",

editor = "R. Rastogi and K. Morik and M. Bramer and X. Wu",

booktitle = "Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004",

note = "Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 ; Conference date: 01-11-2004 Through 04-11-2004",

}

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. Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004, pp. 99-105, Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004, Brighton, United Kingdom, 11/1/04. https://doi.org/10.1109/ICDM.2004.10094

A bayesian framework for regularized SVM parameter estimation. / Gregor, Jens; Liu, Zhenqiu.
Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004. ed. / R. Rastogi; K. Morik; M. Bramer; X. Wu. 2004. p. 99-105 (Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Liu, Zhenqiu

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 9780769521428

T3 - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

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BT - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

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Y2 - 1 November 2004 through 4 November 2004

ER -

A bayesian framework for regularized SVM parameter estimation

Abstract

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All Science Journal Classification (ASJC) codes

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