A novel learning model-Kernel Granular Support Vector Machine

This paper presents a novel machine learning model-kernel granular support vector machine (KGSVM), which combines traditional support vector machine (SVM) with granular computing theory. By dividing granules and replacing with them in kernel space, the datasets can be reduced effectively without changing data distribution. And then the generalization performance and training efficiency of SVM can be improved. Simulation results on UCI datasets demonstrate that KGSVM is highly scalable for large datasets and very effective in terms of classification.