Gradient Weights help Nonparametric Regressors
نویسندگان
چکیده
where An,i(X) ≡ we are confident in both estimates fn,h(X ± tei). •Fast preprocessing, and online: just 2 estimates of fn,h at X . Metric learning optimizes over a space of possible metrics. •Only 2× 2× 1 param. search grid to adapt to d dimensions. KR with bandwidths {hi}d1 needs d× d param. search grid. •General: preprocessing for any distance-based regressor. Other methods apply to particular regressors, e.g. Rodeo for local linear regression, metric learning for KR.
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تاریخ انتشار 2012