Understanding kernel ridge regression: Common behaviors from simple functions to density functionals
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vu, Kevin | - |
dc.contributor.author | Snyder, John C. | - |
dc.contributor.author | Li, Li | - |
dc.contributor.author | Rupp, Matthias | - |
dc.contributor.author | Chen, Brandon F. | - |
dc.contributor.author | Khelif, Tarek | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Burke, Kieron | - |
dc.date.accessioned | 2021-09-04T13:29:46Z | - |
dc.date.available | 2021-09-04T13:29:46Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-08-15 | - |
dc.identifier.issn | 0020-7608 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/92751 | - |
dc.description.abstract | 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. (c) 2015 Wiley Periodicals, Inc. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.subject | NETWORKS | - |
dc.subject | DYNAMICS | - |
dc.title | Understanding kernel ridge regression: Common behaviors from simple functions to density functionals | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1002/qua.24939 | - |
dc.identifier.scopusid | 2-s2.0-84936800623 | - |
dc.identifier.wosid | 000357606000012 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, v.115, no.16, pp.1115 - 1128 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY | - |
dc.citation.title | INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY | - |
dc.citation.volume | 115 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 1115 | - |
dc.citation.endPage | 1128 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Quantum Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Physics, Atomic, Molecular & Chemical | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | DYNAMICS | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | hyperparameters optimization | - |
dc.subject.keywordAuthor | noise-free curve | - |
dc.subject.keywordAuthor | extreme behaviors | - |
dc.subject.keywordAuthor | density functional theory | - |
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