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Kernel regression : ウィキペディア英語版
Kernel regression
: ''Not to be confused with Kernel principal component analysis.''
Kernel regression is a non-parametric technique in statistics to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables ''X'' and ''Y''.
In any nonparametric regression, the conditional expectation of a variable Y relative to a variable X may be written:
\operatorname(Y | X) = m(X)
where m is an unknown function.
== Nadaraya-Watson kernel regression ==
and proposed to estimate m as a locally weighted average, using a kernel as a weighting function. The Nadaraya-Watson estimator is:
\widehat_h(x)=\frac

where K is a kernel with a bandwidth h. The fraction is a weighting term with sum 1.

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 regression」の詳細全文を読む



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