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

Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal