Support vector machine classifiers by non-Euclidean margins

Abstract

In this article, the classical support vector machine (SVM) classifiers are generalized by the non-Euclidean margins. We first extend the linear models of the SVM classifiers by the non-Euclidean margins including the theorems and algorithms of the SVM classifiers by the hard margins and the soft margins. Specially, the SVM classifiers by the $ \infty $-norm margins can be solved by the 1-norm optimization with sparsity. Next, we show that the non-linear models of the SVM classifiers by the $ q $-norm margins can be equivalently transferred to the SVM in the $ p $-norm reproducing kernel Banach spaces given by the hinge loss, where $ 1/p+1/q = 1 $. Finally, we illustrate the numerical examples of artificial data and real data to compare the different algorithms of the SVM classifiers by the $ \infty $-norm margin.