In this paper,L_1 norm is employed to loose the bound of the VC dimension,and thus a new structure risk based on the L_1 norm is developed.

Support Vector machines (SVM) operate on the principle of structure risk minimization which not only keeps the empirical risk minimal but also controls VC confidence of discriminant functions,hence a better generalization ability is guaranteed.

The structural risk was considered in the goal function to avoid overfitting in traditional algorithms and then the parameter estimation of a Mamdani fuzzy system was converted to a quadratic optimization problem.

Based on the structural risk theory,a quantity estimation is proposed though empirical risk and confidence interval,and an evaluation formula for kernel function is given.

The influence of generation ability based on the empirical risk minimization and the support vector machines based on the structural risk minimization are discussed.

Study on risk structure of hi-tech industry s modulization;

Analysis of the Industry s Risk Structure of the China Stock Market;

Generally speaking,the maturity structure and risk structure of bond markets are the standards to compare the width and depth of bond markets.

Based on analysis of the conclusions in the statistical learning theory,especially the structural risk minimization and the ε-insensitive loss function,a novel linear programming support vector regression is proposed.