“…The performance of traditional statistical methods can be guaranteed only when the number of samples tends to infinity, and it is difficult to obtain ideal results with limited samples in practical application, however, SVM has achieved very good results in this field (Ma et al, 2016;Zhang et al, 2012;). In addition, the complexity of the SVM is related to the number of support vectors, therefore, usually, over fitting won't happen to SVM (Lin et al, 2015;Tian et al, 2017). SVM is developed from the optimal separating hyperplane in the case of linear separability, it's basic idea can be illustrated by the two classification situations in Figure 3.…”