Polar support vector machine: Single and multiple outputs

Abstract

Support vector machine (SVM) is a popular supervised learning algorithm achieved much success in classification problems in both single output and multi-output cases. Despite this fact, SVM has some limitations, for example, in terms of the accuracy when data are not linearly separable. One of the well-known tricks for tackling this obstacle is using kernel functions in the base model of SVM which map all data into a higher or infinite dimension space called feature space. We propose polar coordinate system as an efficient technique for mapping data and improving the accuracy of SVM in some datasets. Hence, the current research is assigned to extending SVM model to polar one for single and multi-output cases. Also, the proposed SVM may be combined with kernel functions to outperform traditional non-linear SVM. The performance of the proposed model is compared with traditional one to affirm the superiority of polar SVM.