支持向量机是 Cortes 和 Vapnic 于 1995 年首先提出的47它在解决小样本、高维模式识别及非线性中表现出独特的优势并能拟合推广到其他函数应用当中48。小样本并不是说样本数量绝对少而是说与问题复杂度相比SVM 算法要求的样本数量相对较少。对上面这段话进行同义改写

Support Vector Machine (SVM) was initially proposed by Cortes and Vapnic in 1995 [47], and it has demonstrated unique advantages in solving small-sample, high-dimensional pattern recognition, and non-linearity, which can be extended to other function applications [48]. Small-sample does not necessarily mean an absolute shortage of sample quantity, but rather that the required sample quantity by the SVM algorithm is relatively small compared to the complexity of the problem.