说明：  支持向量机最初用来解决模式识别问题，目的是发现泛化性能好的决策规则。然而，随着Vapnik的 不敏感损失函数的引入，支持向量机已经扩展为解决非线性回归估计问题，而且与神经网络方法相比，有着显著的优越性，被认为是人工神经网络方法的替代方法，已经成为目前机器学习领域的研究热点和焦点。
(Support vector machine (SVM) was originally used to solve the problem of pattern recognition. However, with the introduction of Vapnik's insensitive loss function, support vector machine (SVM) has been extended to solve the problem of nonlinear regression estimation. Compared with neural network method, support vector machine (SVM) has obvious advantages. It is considered as an alternative method of artificial neural network method, and has become the research hotspot and focus in the field of machine learning.)