中文说明：支持向量机（Support Vector Machines ,SVM）由 Vapnik 1995年最早提出，通过核函数的展开定理，在某种程度上避免了“维数灾难”，并且在解决小样本、非线性和高维模式识别中表现出了独有的优势，引起很多学者的重视，取得一定的研究成果,并且被许多学者推广应用到其他机器学习领域中。
English Description：
Support vector machines (SVM) was first proposed by Vapnik in 1995. Through the expansion theorem of kernel function, it avoids the "dimension disaster" to some extent, and shows its unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition, which has attracted the attention of many scholars and achieved certain research results, And it has been applied to other machine learning fields by many scholars.