The universal consistency is proved for nonparametric regression and nonparametric decision procedures in real-life information processing. More distinctly, strong and weak universal consistency of nonparametric regression estimate and multiple classification by the nearest neighbor and kernel estimates are investigated.
|Number of pages||10|
|Journal||Problems of control and information theory|
|Publication status||Published - Jan 1 1981|
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