Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/50932
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dc.contributor.authorChew, H.-
dc.contributor.authorLim, C.-
dc.date.issued2009-
dc.identifier.citationJournal of Industrial and Management Optimization, 2009; 5(2):403-415-
dc.identifier.issn1547-5816-
dc.identifier.issn1553-166X-
dc.identifier.urihttp://hdl.handle.net/2440/50932-
dc.description.abstractThe 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.-
dc.description.statementofresponsibilityHong-Gunn Chew and Cheng-Chew Lim-
dc.language.isoen-
dc.publisherAmerican Institute of Mathematical Sciences-
dc.source.urihttp://dx.doi.org/10.3934/jimo.2009.5.403-
dc.subjectSupport Vector Machine-
dc.subjectPattern recognition-
dc.subjectQuadratic optimisation.-
dc.titleOn regularisation parameter transformation of support vector machines-
dc.typeJournal article-
dc.identifier.doi10.3934/jimo.2009.5.403-
pubs.publication-statusPublished-
dc.identifier.orcidChew, H. [0000-0001-6525-574X]-
dc.identifier.orcidLim, C. [0000-0002-2463-9760]-
Appears in Collections:Aurora harvest 5
Electrical and Electronic Engineering publications

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