Support vector machine (SVM) is one of the most popular machine learning methods and educed from a binary data classification problem. Actually, it is a quadratic programming with linear inequalities. Canonical duality theory is a potential powerful methodology developed recently which can be used for solving a large class of challenging problems in complex systems. This special session focus on the applications of the canonical duality theory to solve the normal model of SVM.