Abstract: The used SVM kernel function can not approach to any object function in regression.Aiming at the problem，the construction model of Least Square Wavelet Support Vector Machine（LS-WSVM） is proposed based on conditions of the support vector kernel function and wavelet frame theory.Compared with standard SVM and LS-SVM under the same conditions，experimental results show that LS-WSVM has more excellent performance of feature abstraction in regression and its fitting result is more accurate.

摘要： 针对目前使用的SVM核函数在回归中不能逼近任意目标函数的问题，在支持向量机的核方法和小波框架理论的基础上，提出了LS-WSVM结构模型。该模型在LS－SVM中使用一种新的由小波构成的SVM核函数。实验结果表明，与标准的SVM及LS－SVM比较起来，在同等条件下，LS-WSVM在函数回归方面LS-WSVM具有优良的逼近性能，拟合效果更为细腻。