  Support Vector Machine (SVM) is an efficient classification approach, which
finds a hyperplane to separate data from different classes. This hyperplane is
determined by support vectors. In existing SVM formulations, the objective
function uses L2 norm or L1 norm on slack variables. The number of support
vectors is a measure of generalization errors. In this work, we propose a
Minimal SVM, which uses L0.5 norm on slack variables. The result model further
reduces the number of support vectors and increases the classification
performance.
