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・ Kerplunk (album)
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・ Kernel methods for vector output
・ Kernel Normal Form
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Kernel smoother
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Kernel smoother : ウィキペディア英語版
Kernel smoother

A kernel smoother is a statistical technique for estimating a real valued function f(X)\,\,\left( X\in \mathbb^ \right) 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 K_(X_0 ,X) be a kernel defined by
:K_(X_0 ,X) = D\left( \frac \right)
where:
* X,X_0 \in \mathbb^p
* \left\| \cdot \right\| is the Euclidean norm
* h_\lambda (X_0) 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)
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