Research on multi-classification based on support vector machine

TLDR: This paper introduced the support vector machine(SVM) based on the theory of traditional statistics that can solve small-sample learning problems better by using experiential risk minimization (ERM) in place of structural risk minimizations (SRM) and can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea.

Abstract: Most of the existing methods are based on traditional statistics,which provides that conclusion only for the situation where sample size is tending to infinity.So they may not work well in practical case with limi-ted samples and easily lead to the problem of overfilling.This paper introduced the support vector machine(SVM) based on the theory of traditional statistics.This method can solve small-sample learning problems better by using experiential risk minimization(ERM) in place of structural risk minimization(SRM).Moreover,this theory can change the problem in non-linearity space to that in the linearity space in order to reduce the algorithm complexity by using the kernel function idea.It studies some relational contents including the optimization algorithm and the solution to multi-classification.Finally,through an example,it shows that the pro-posed method is effective and feasible.