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Type: Journal article
Title: On regularisation parameter transformation of support vector machines
Author: Chew, H.
Lim, C.
Citation: Journal of Industrial and Management Optimization, 2009; 5(2):403-415
Publisher: American Institute of Mathematical Sciences
Issue Date: 2009
ISSN: 1547-5816
Statement of
Hong-Gunn Chew and Cheng-Chew Lim
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: Support Vector Machine
Pattern recognition
Quadratic optimisation.
DOI: 10.3934/jimo.2009.5.403
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Appears in Collections:Aurora harvest 5
Electrical and Electronic Engineering publications

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