An asymptotically minimax kernel machine

Debashis Ghosh

Statistics & Probability Letters, 2014, vol. 95, issue C, 33-38

Abstract: Recently, a class of machine learning-inspired procedures, termed kernel machine methods, has been extensively developed in the statistical literature. In this note, we construct a so-called ‘adaptively minimax’ kernel machine. Such a construction highlights the limits on the interpretability of such kernel machines.

Keywords: Data mining; Decision theory; Hard-thresholding; Nonparametric regression; Support vector machines (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.spl.2014.08.005

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