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A kernel smoother is a statistical technique for estimating a real valued function by using its noisy observations, when no parametric model for this function is known. The estimated function is smooth, and the level of smoothness is set by a single parameter. This technique is most appropriate for low-dimensional (''p'' < 3) data visualization purposes. Actually, the kernel smoother represents the set of irregular data points as a smooth line or surface. ==Definitions== Let be a kernel defined by : where: * * is the Euclidean norm * is a parameter (kernel radius) * ''D''(''t'') typically is a positive real valued function, which value is decreasing (or not increasing) for the increasing distance between the ''X'' and ''X''0. Popular kernels used for smoothing include * 抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)』 ■ウィキペディアで「Kernel smoother」の詳細全文を読む スポンサード リンク
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