Structure risk minimization based weighted partial least-squared method
基于结构风险最小化的加权偏最小二乘法

Weighted Partial Least-Squared (WPLS) method was proposed to achieve Structure Risk Minimization(SRM)in the Partial Least-Squares (PLS) modeling process.At first,WPLS abstracted the principal components of training samples,and then it trained the weight of samples by means of Support Vector Machine (SVM) algorithm,and finally computed the regression model in the original universe discourse.WPLS not only takes the advantage of PLS to extract most explanatory variables,but also improves generalization property through the weight of samples and SRM is achieved with interpretable model.Simulation results show the effectiveness of the proposed method.