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Kernel smoothing techniques, such as kernel density estimation (KDE) and Nadaraya-Watson kernel regression, estimate functions by local interpolation from data points. Not to be confused with [kernel-trick], for the kernels used e.g. in SVMs.

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I've used Nadaraya-Watson Kernel regression before to smooth data. Recently I have run into Gaussian process regression. Prima facie, they don't seem to be related. But I am wondering if there perh …
asked Oct 28 '16 by Demetri Pananos