The subroutines glkern.f and lokern.f use an efficient and fast algorithm for automatically adaptive nonparametric regression estimation with a kernel method. Roughly speaking, the method performs a local averaging of the observations when es

局部线性回归方法及其稳健形式已经被看作一种有效的非参数光滑方法.与流行的核回归方法相比,它有诸多优点,诸如:较高的渐近效率和较强的适应设计能力.另外,局部线性回归能适应几乎所有的回归设计情形却不需要任何边界修正。,Local linear regression methods and their solid form has been seen as an effective non-parametric smoothing method. Contrary to popular kernel

基于SVM数据分类及回归分析，并采用不同的核函数如RBF,sigmoid,polynomial等-the data classification and regression analysis based on SVM, by using different kinds of kernel functions, for examples, RBF,sigmoid and ploynomial and so on