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dc.contributor.authorChimienti, Marianna
dc.contributor.authorCornulier, Thomas
dc.contributor.authorOwen, Ellie
dc.contributor.authorBolton, Mark
dc.contributor.authorDavies, Ian M.
dc.contributor.authorTravis, Justin M. J.
dc.contributor.authorScott, Beth E.
dc.date.accessioned2016-02-09T15:00:02Z
dc.date.available2016-02-09T15:00:02Z
dc.date.issued2016-02
dc.identifier.citationChimienti , M , Cornulier , T , Owen , E , Bolton , M , Davies , I M , Travis , J M J & Scott , B E 2016 , ' The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data ' , Ecology and Evolution , vol. 6 , no. 3 , pp. 727–741 . https://doi.org/10.1002/ece3.1914en
dc.identifier.issn2045-7758
dc.identifier.otherPURE: 62402579
dc.identifier.otherPURE UUID: 6ea221f6-cc7c-4173-93ba-7d556e768ade
dc.identifier.otherScopus: 84956717441
dc.identifier.urihttp://hdl.handle.net/2164/5503
dc.descriptionAcknowledgments This project and the tags deployed on both seabird's species were fund by NERC (grant number NE/K007440/1), Marine Scotland Science and Seabird Tracking and Research (STAR) Project led by the Royal Society for the Protection of Birds (RSPB). We would like to thank Rob Hughes, Tessa Cole and Ruth Brown for helping in the data collection, the Bird Observatory of Fair Isle for supporting the fieldwork and the Marine Collaboration Research Forum (MarCRF).en
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofEcology and Evolutionen
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0/en
dc.subjectAccelerometer dataen
dc.subjectanimal movementsen
dc.subjectbehavioral classificationen
dc.subjectunsupervised learningen
dc.subjectQH301 Biologyen
dc.subjectNatural Environment Research Council (NERC)en
dc.subjectNE/K007440/1en
dc.subject.lccQH301en
dc.titleThe use of an unsupervised learning approach for characterizing latent behaviors in accelerometer dataen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Medical Statisticsen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1002/ece3.1914


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