The conic-segmentation support vector machine - a target space method for multiclass classification

In this paper we propose a new multiclass SVM, the conic-segmentation SVM (CS-SVM), based on the direct mapping of points into a multidimensional target space segmented a-priori into conic class regions defined by generalized inequalities. We show that the CS-SVM is a natural multiclass analogue of the standard binary SVM in-so-far as it shares its motivation, simplicity of form, and many of its properties such as convexity, sparsity and kernelisation. We demonstrate that prior selection of the conic region structure can give both new and interesting multiclass formulations and also well-known multiclass formulations. Finally we present experimental results on artificial and real multiclass datasets to investigate the CS-SVM's performance.