Piecewise-Smooth Support Vector Machine for Classification

Abstract EN

Support vector machine (SVM) has been applied very successfully in a variety of classification systems.

We attempt to solve the primal programming problems of SVM by converting them into smooth unconstrained minimization problems.

In this paper, a new twice continuously differentiable piecewise-smooth function is proposed to approximate the plus function, and it issues a piecewise-smooth support vector machine (PWSSVM).

The novel method can efficiently handle large-scale and high dimensional problems.

The theoretical analysis demonstrates its advantages in efficiency and precision over other smooth functions.

PWSSVM is solved using the fast Newton-Armijo algorithm.

Experimental results are given to show the training speed and classification performance of our approach.