American Institute of Mathematical Sciences

Advanced
April  2009, 5(2): 403-415. doi: 10.3934/jimo.2009.5.403

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, Australia, Australia

Received  March 2008 Revised  September 2008 Published  April 2009

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: Support Vector Machine, Quadratic optimisation., Pattern recognition.
Mathematics Subject Classification: Primary: 68T10; Secondary: 90C2.
Citation: Hong-Gunn Chew, Cheng-Chew Lim. On regularisation parameter transformation of support vector machines. Journal of Industrial and Management Optimization, 2009, 5 (2) : 403-415. doi: 10.3934/jimo.2009.5.403
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