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: ''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 relative to a variable may be written: where is an unknown function. == Nadaraya-Watson kernel regression == and proposed to estimate as a locally weighted average, using a kernel as a weighting function. The Nadaraya-Watson estimator is: where is a kernel with a bandwidth . 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 スポンサード リンク
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