Abstract
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.
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Liang, J., Wu, D. (2010). A New Smooth Support Vector Machine. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_32
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DOI: https://doi.org/10.1007/978-3-642-16530-6_32
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