Causality, machine learning and human insight
| dc.contributor.author | Martens, Harald | |
| dc.date.accessioned | 2024-06-06T11:10:23Z | |
| dc.date.available | 2024-06-06T11:10:23Z | |
| dc.date.created | 2023-08-14T14:05:57Z | |
| dc.date.issued | 2023 | |
| dc.identifier.citation | Analytica Chimica Acta. 2023, 1277 . | en_US |
| dc.identifier.issn | 0003-2670 | |
| dc.identifier.uri | https://hdl.handle.net/11250/3132859 | |
| dc.description.abstract | Modern instruments generate BIG DATA that require information extraction before they can be used. A hybrid modelling framework for that is presented and illustrated. Its purpose is to convert meaningless data to meaningful information and to contribute to a theoretical, practical, and democratic basis for tomorrow's handling of BIG DATA in science and technology. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Elsevier | en_US |
| dc.rights | Navngivelse 4.0 Internasjonal | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
| dc.title | Causality, machine learning and human insight | en_US |
| dc.title.alternative | Causality, machine learning and human insight | en_US |
| dc.type | Peer reviewed | en_US |
| dc.type | Journal article | en_US |
| dc.description.version | acceptedVersion | en_US |
| dc.rights.holder | © 2023 Elsevier B.V. All rights reserved. | en_US |
| dc.source.pagenumber | 7 | en_US |
| dc.source.volume | 1277 | en_US |
| dc.source.journal | Analytica Chimica Acta | en_US |
| dc.identifier.doi | 10.1016/j.aca.2023.341585 | |
| dc.identifier.cristin | 2166790 | |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 1 |
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