Support conformal vector machines with optimal Bayes point

This work design support vector machines using the conformal Clifford geometric algebra framework. In this study we map the feature space into hyperspheres in order to get a uniformly distribution data. In this domain we apply as classifier a support conformal vector machines. In this context the optimal hyperplane found by the support conformal vector machine will approach to the optimal Bayes point. An experimental analysis clarifies our approach.