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Kernel smoothing M.C. Jones, M.P. Wand
Publisher: Chapman & Hall

In a software application I am attempting to smooth a data set by convoluting it with a discrete Gaussian kernel. Regularization is of supreme importance in modeling in general. Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. Regular workshop run at the Australian Antarctic Division to assist those trying to develop skills in the statistical package R. Re-weighting of data by smoothing kernels (different but related use of the work “kernel”) is central to non-parametric statistics (kernel smoothers and splines). In the next session Simon Wotherspoon will discuss kernel density estimation and kernel smoothing. The estimated function is smooth, and the level of smoothness is set by a single parameter. ASM Lesson 25, Kernel Smoothing Exam 4/C - Construction and Evaluation of Actuarial Models. Or I need something like radial basis function (with spline-tension kernel) / kernel smoothing interpolation method? Interpolation (or any other tool) to make my network finer and only then to interpolate my values? Little or no training is required for operation of the kernel smoother.