When solving this small sample problem with high dimension and nonlinear, many traditional pattern recognition methods will tend to occur overfitting phenomenon.
对于这样一个高维数、非线性的小样本问题， 许多传统的模式识别方法都容易出现过学习或欠学习现象。
Absrtact: SVM represented many unique advantages in many applications, such as solving the problem of nonlinear, high dimension pattern recognition and small sample problem.
摘要：在解决非线性、高维模式识别以及小样本等问题中，支持向量机表现出许多独有的优势。
SVM solves the small sample problem mainly and finds the best compromise between the complexity of the model and the learning ability in order to obtaining the best generalization ability.
SVM主要解决小样本问题，在模型的复杂度和学习能力之间寻求最佳折衷，目的在于获得最好的泛化能力。
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