Publikation: Understanding kernel ridge regression : Common behaviors from simple functions to density functionals
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Datum
2015
Autor:innen
Vu, Kevin
Snyder, John C.
Li, Li
Chen, Brandon F.
Khelif, Tarek
Müller, Klaus-Robert
Burke, Kieron
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International Journal of Quantum Chemistry. Wiley-Blackwell. 2015, 115(16), pp. 1115-1128. ISSN 0020-7608. eISSN 1097-461X. Available under: doi: 10.1002/qua.24939
Zusammenfassung
Accurate approximations to density functionals have recently been obtained via machine learning (ML). By applying ML to a simple function of one variable without any random sampling, we extract the qualitative dependence of errors on hyperparameters. We find universal features of the behavior in extreme limits, including both very small and very large length scales, and the noise‐free limit. We show how such features arise in ML models of density functionals.
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004 Informatik
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machine learning, hyperparameters optimization, noise‐free curve, extreme behaviors, density functional theory
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VU, Kevin, John C. SNYDER, Li LI, Matthias RUPP, Brandon F. CHEN, Tarek KHELIF, Klaus-Robert MÜLLER, Kieron BURKE, 2015. Understanding kernel ridge regression : Common behaviors from simple functions to density functionals. In: International Journal of Quantum Chemistry. Wiley-Blackwell. 2015, 115(16), pp. 1115-1128. ISSN 0020-7608. eISSN 1097-461X. Available under: doi: 10.1002/qua.24939BibTex
@article{Vu2015Under-52126,
year={2015},
doi={10.1002/qua.24939},
title={Understanding kernel ridge regression : Common behaviors from simple functions to density functionals},
number={16},
volume={115},
issn={0020-7608},
journal={International Journal of Quantum Chemistry},
pages={1115--1128},
author={Vu, Kevin and Snyder, John C. and Li, Li and Rupp, Matthias and Chen, Brandon F. and Khelif, Tarek and Müller, Klaus-Robert and Burke, Kieron}
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