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

On regularisation parameter transformation of support vector machines

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.
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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