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DC programming and DCA for parametric-margin ν-support vector machine
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DOI：10.1007/s10489-019-01618-x.png)
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Abstract
En 中文
As a development of nu-support vector machine (nu-SVM), parametric-margin nu-support vector machine (Par-nu-SVM) can be useful in many cases, especially heteroscedastic noise classification problems. The present article proposes a novel and fast method to solve the primal problem of Par-nu-SVM (named as DC-Par-nu-SVM), while Par-nu-SVM maximizes the parametric-margin by solving a dual quadratic programming problem. In fact, the primal non-convex problem is converted into an unconstrained problem to express the objective function as the difference of convex functions (DC). The DC-Algorithm (DCA) based on generalized Newton's method is proposed to solve the unconstrained problem cited. Numerical experiments performed on several artificial, real-life, UCI and NDC data sets showed the superiority of the DC-Par-nu-SVM in terms of both accuracy and learning speed.
Keywords:
Support vector machine
Non-convex optimization
Generalized Newton's method
DC programming
DCA
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