SVM

  　支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度，Accuracy）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力[14]（或称泛化能力）。 (SVM is based on statistical learning theory and the theory of VC dimension based on structural risk minimization principle, according to the limited sample of information in the model complexity (ie, training samples of a specific learning accuracy, Accuracy) and learning ability (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability [14] (or generalization).)