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Published in:
2010 | OriginalPaper | Chapter
A New Smooth Support Vector Machine
Authors : Jinjin Liang, De Wu
Published in: Artificial Intelligence and Computational Intelligence
Publisher: Springer Berlin Heidelberg
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A new Smooth Support Vector Machine (SSVM) is proposed and is called NSSVM for short. Different from traditional SSVM that treats perturbation formulation of SVM, NSSVM treats standard 2-norm error soft margin SVM. Different from traditional SSVM that uses the 2-norm of the Lagrangian multipliers vector to roughly substitute that of the weight of the separating hyperplane, which makes the obtained smooth model unequal to the primal program; NSSVM takes into account the connotative relation between the primal and dual program to transform the original program to a new smooth one. Numerical experiments on several UCI datasets demonstrate that NSSVM has higher precisions than existing methods.