A role of total margin in support vector machines

The support vector algorithm has paid attention on maximizing the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithm which considers the distance between all data points and the separating hyperplane. The method extends existing support vector machine algorithms. In addition, the method improves the generalization error bound. Numerical studies show that the total margin algorithm provides good performance, comparing with the previous methods.