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|Scopus||Web of Science®||Altmetric|
|Title:||On regularisation parameter transformation of support vector machines|
|Citation:||Journal of Industrial and Management Optimization, 2009; 5(2):403-415|
|Publisher:||American Institute of Mathematical Sciences|
|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|
|Appears in Collections:||Aurora harvest 5|
Electrical and Electronic Engineering publications
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