On regularisation parameter transformation of        support vector machines

Hong-Gunn Chew; Cheng-Chew Lim

doi:10.3934/jimo.2009.5.403

Article Contents

2009, Volume 5, Issue 2: 403-415. Doi: 10.3934/jimo.2009.5.403

This issue Previous Article Censored newsvendor model revisited with unnormalized probabilities Next Article

On regularisation parameter transformation of support vector machines

Hong-Gunn Chew^1, and
Cheng-Chew Lim^1,

1.
School of Electrical and Electronic Engineering, The University of Adelaide, SA 5005

Received: March 2008

Revised: September 2008

Published: April 2009

Abstract Related Papers Cited by

Abstract

The Dual-nu Support Vector Machine (SVM) is an effective method in pattern recognition and target detection. It improves on the Dual-C SVM, and offers competitive performance in detection and computation with traditional classifiers. We show that the regularisation parameters Dual-nu and Dual-C can be set such that the same SVM solution is obtained. We present the process of determining the related parameters of one form from the solution of a trained SVM of the other form, and test the relationship with a digit recognition problem. The link between the Dual-nu and Dual-C parameters allows users to use Dual-nu for ease of training, and to switch between the two forms readily.

Keywords:

Mathematics Subject Classification: Primary: 68T10; Secondary: 90C20.

Citation:

Article Metrics

HTML views() PDF downloads(80) Cited by(0)

On regularisation parameter transformation of support vector machines

Abstract

Access History

Article Metrics

Access History

Other Articles By Authors

Catalog

On regularisation parameter transformation of support vector machines

Abstract

Access History

SHARE

Article Metrics

Access History

Other Articles By Authors

Catalog

Export File

Citation

Format

Content