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Improving Enterprise Resource Planning results using knowledge extraction and learning

dc.contributor.authorBerzosa Arnáiz, Alba
dc.contributor.authorSedano Franco, Javier
dc.contributor.authorVillar Flecha, José Ramón 
dc.contributor.authorGarcía Tamargo, Marco Antonio 
dc.contributor.authorCorchado, Emilio
dc.date.accessioned2016-03-22T13:15:55Z
dc.date.available2016-03-22T13:15:55Z
dc.date.issued2010
dc.identifier.citationProceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010, p. 340-344 (2010); doi:10.1109/NABIC.2010.5716273
dc.identifier.isbn9781424473762
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-79952774257&partnerID=40&md5=2f9984bda0c8510b34fab619d1151019
dc.identifier.urihttp://hdl.handle.net/10651/36237
dc.descriptionWorld Congress on Nature and Biologically Inspired Computing, NaBIC 2010 (2nd. 2010. Kitakyushu, Japan)
dc.description.sponsorshipThis research work has been funded by the Spanish Ministry of Science and Innovation, under grant TIN2008-06681-C06-04 and the Spanish Ministry of Science and Innovation through project PIO 560300-2009-11 . This research is also partially supported by projects TIN2010-21272-C02-01 from the Spanish Ministry of Science and Innovation and BU006A08 of the JCyL
dc.format.extentp. 340-344
dc.language.isoeng
dc.relation.ispartofProceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
dc.rights©,
dc.titleImproving Enterprise Resource Planning results using knowledge extraction and learning
dc.typeconference outputspa
dc.identifier.doi10.1109/NABIC.2010.5716273
dc.accrualPeriodicityMICINN/TIN2008-06681-C06-04
dc.relation.publisherversionhttp://dx.doi.org/10.1109/NABIC.2010.5716273


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