A New Smooth Support Vector Machine
AICI'10: Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I(2010)
摘要
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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关键词
Smooth Support Vector Machine,2-norm error soft margin SVM,connotative relation,primal and dual program
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