When solving this small sample problem with high dimension and nonlinear, many traditional pattern recognition methods will tend to occur overfitting phenomenon.

Absrtact: SVM represented many unique advantages in many applications, such as solving the problem of nonlinear, high dimension pattern recognition and small sample problem.

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.