Lepskii Principle in Supervised Learning

Gilles Blanchard; Peter Mathé; Nicole Mücke

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arXiv · 1905.10764

Lepskii Principle in Supervised Learning

Gilles Blanchard·Peter Mathé·Nicole Mücke

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Abstract

In the setting of supervised learning using reproducing kernel methods, we propose a data-dependent regularization parameter selection rule that is adaptive to the unknown regularity of the target function and is optimal both for the least-square (prediction) error and for the reproducing kernel Hilbert space (reconstruction) norm error. It is based on a modified Lepskii balancing principle using a varying family of norms.

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Gilles Blanchard, Peter Mathé, Nicole Mücke. 2019-05-26. Lepskii Principle in Supervised Learning. https://arxiv.org/abs/1905.10764

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