Using regression trees to predict citrus load balancing accuracy and costs

dc.contributor.author	Bóbeda, G. R. R.
dc.contributor.author	Fernández-Combarro Álvarez, Elías
dc.contributor.author	Mazza, S.
dc.contributor.author	Giménez, L. I.
dc.contributor.author	Díaz Rodríguez, Susana Irene
dc.date.accessioned	2019-04-04T08:30:15Z
dc.date.available	2019-04-04T08:30:15Z
dc.date.issued	2018
dc.identifier.citation	International Journal of Computational Intelligence Systems, 12(1), p. 79-89 (2018); doi:10.2991/ijcis.2018.25905183
dc.identifier.issn	1875-6891
dc.identifier.uri	http://hdl.handle.net/10651/50973
dc.description.sponsorship	This work was supported by Excellence project TIN2017-87600-P from the Ministry of Economy, industry and competitiveness of Spain.
dc.format.extent	p. 79-89	spa
dc.language.iso	eng	spa
dc.relation.ispartof	International Journal of Computational Intelligence, 12(1)	spa
dc.rights	© 2018, the Authors. Published by Atlantis Press
dc.rights	CC Reconocimiento 4.0 Internacional
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/
dc.title	Using regression trees to predict citrus load balancing accuracy and costs	spa
dc.type	journal article	spa
dc.relation.projectID	TIN2017-87600-P	spa
dc.relation.projectID	FICYT/IDI/2018/000176
dc.rights.accessRights	open access	spa
dc.type.hasVersion	AM

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