Deterministic error bounds for kernel-based learning techniques under bounded noise

We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and epsilon-support vector regression. By assuming the ground-truth function belongs to the reproducing kernel Hilbert space of the chosen kernel, and the measurement noise affecting the dataset is bounded, we adopt an approximation theory viewpoint to establish deterministic, finite-sample error bounds for the two models. Finally, we discuss their connection with Gaussian processes and two numerical examples are provided. In establishing our inequalities, we hope to help bring the fields of non-parametric kernel learning and system identification for robust control closer to each other. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper discusses the problem of reconstructing a function from noise-corrupted samples, analyzing two kernel algorithms- kernel ridge regression and epsilon-support vector regression. By establishing finite-sample error bounds and providing numerical examples, the connection between these algorithms and Gaussian processes is explored, aiming to bridge the gap between non-parametric kernel learning and system identification for robust control.