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Found 2 papers, 1 papers with code
Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
We treat the problem of choosing the hyperparameter as an iterative procedure (over $k$) and propose using an easily implemented in practice strategy based on the idea of early stopping and the minimum discrepancy principle.
Early stopping and polynomial smoothing in regression with reproducing kernels
In this paper, we study the problem of early stopping for iterative learning algorithms in a reproducing kernel Hilbert space (RKHS) in the nonparametric regression framework.
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