摘要
在传统支持向量机的分类求解算法中,严格凸的无约束最优化问题中单变量函数+是不可微的。三次样条插值多项式光滑的支持向量机模型采用的是一种多项式光滑技术,使用三次样条插值二次多项式来逼近单变量函数+,将原始非光滑的支持向量机模型转化为光滑模型,从而可以使用最常用的最优化的算法进行求解,给出了使用三次样条插值方法来光滑单变量函数的具体推导过程。使用UCI机器学习数据集中的数据,通过实验验证了该模型的有效性。
In traditional SVM solution algorithms, objective function is a strictly convex unconstrained optimization problem, but is not differentiable due to x~, which precludes the use of most used optimization algorithms. Polynomial smooth techniques are applied to SVM model and replace x. by a very accurate smooth approximation that is cubic spline interpolation polynomial, thus the undifferential model is converted into a differential model. The deduction procedure of cubic spline interpolation polynomial smoothing x. is extended. Experiments with UC1 datasets show the validity of the model.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第7期1722-1724,1734,共4页
Computer Engineering and Design
基金
河南工业大学校基金项目(07XJC042
07XGG012)
