Search Results for author: Klaus Robert Müller

Found 1 papers, 0 papers with code

Optimal Sampling Density for Nonparametric Regression

no code implementations25 May 2021 Danny Panknin, Klaus Robert Müller, Shinichi Nakajima

Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as the locally optimal bandwidths (LOB) - the bandwidth function that minimizes MISE in the asymptotic limit.

Active Learning regression

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