On regularisation parameter transformation of        support vector machines

Hong-Gunn Chew; Cheng-Chew Lim

doi:10.3934/jimo.2009.5.403

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

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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: February 29, 2008

Revised: August 31, 2008

Published: March 2009

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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.

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

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