摘要
分析现有支持向量回归方法的缺点和不足,给出一种改进的加权型支持向量回归方法及其Wolfe对偶形式.引入凸函数降低对核函数的要求,并讨论当这些凸函数取不同形式时支持向量回归机的变形,为得到更为灵活的回归曲线提供有效工具.同时对广泛的支持向量回归模型、优化支持向量模型的泛化能力和运算速度等方面进行讨论.
The disadvantage and insufficiency of the existing methods of support vector regression （SVM） are analyzed. An improved weighted support vector regression and its Wolfe Dual is presented. The convex functions and its reformation depending on the various types of the kernel function are introduced to decrease the limitation of the kernel function. It is helpful to get more effective in searching flexible regression function. The generalization of the support vector regression model, the optimization of the generalization capacity, and the training speed are discussed.
出处
《计算机辅助工程》
2006年第1期31-33,共3页
Computer Aided Engineering
关键词
支持向量机
回归
凸函数
核函数
support vector machine （SVM）
regression
convex function
kernel function
