Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

Jeantet, L.; Dell'Amico, F.; Forin-Wiart, M.-A.; Coutant, M.; Bonola, M.; Etienne, D.; Gresser, J.; Regis, S.; Lecerf, N.; Lefebvre, F.; Thoisy, B. de; Maho, Y. Le; Brucker, M.; Châtelain, N.; Laesser, R.; Crenner, F.; Handrich, Y.; Wilson, R.; Chevallier, D.; Wilson, Rory

doi:10.1242/jeb.177378

Journal article 848 views

Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

L. Jeantet, F. Dell'Amico, M.-A. Forin-Wiart, M. Coutant, M. Bonola, D. Etienne, J. Gresser, S. Regis, N. Lecerf, F. Lefebvre, B. de Thoisy, Y. Le Maho, M. Brucker, N. Châtelain, R. Laesser, F. Crenner, Y. Handrich, R. Wilson, D. Chevallier, Rory Wilson

The Journal of Experimental Biology, Volume: 221, Issue: 10, Start page: jeb177378

Swansea University Author: Rory Wilson

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DOI (Published version): 10.1242/jeb.177378

Published in:	The Journal of Experimental Biology
ISSN:	0022-0949 1477-9145
Published:	2018
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa40638
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first_indexed	2018-06-05T19:30:50Z
last_indexed	2018-08-09T12:54:07Z
id	cronfa40638
recordtype	SURis
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spelling	2018-08-09T09:03:19.0950520 v2 40638 2018-06-05 Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data 017bc6dd155098860945dc6249c4e9bc 0000-0003-3177-0177 Rory Wilson Rory Wilson true false 2018-06-05 SBI Journal Article The Journal of Experimental Biology 221 10 jeb177378 0022-0949 1477-9145 23 5 2018 2018-05-23 10.1242/jeb.177378 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2018-08-09T09:03:19.0950520 2018-06-05T15:29:12.8911364 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences L. Jeantet 1 F. Dell'Amico 2 M.-A. Forin-Wiart 3 M. Coutant 4 M. Bonola 5 D. Etienne 6 J. Gresser 7 S. Regis 8 N. Lecerf 9 F. Lefebvre 10 B. de Thoisy 11 Y. Le Maho 12 M. Brucker 13 N. Châtelain 14 R. Laesser 15 F. Crenner 16 Y. Handrich 17 R. Wilson 18 D. Chevallier 19 Rory Wilson 0000-0003-3177-0177 20
title	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
spellingShingle	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data Rory Wilson
title_short	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
title_full	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
title_fullStr	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
title_full_unstemmed	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
title_sort	Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data
author_id_str_mv	017bc6dd155098860945dc6249c4e9bc
author_id_fullname_str_mv	017bc6dd155098860945dc6249c4e9bc_***_Rory Wilson
author	Rory Wilson
author2	L. Jeantet F. Dell'Amico M.-A. Forin-Wiart M. Coutant M. Bonola D. Etienne J. Gresser S. Regis N. Lecerf F. Lefebvre B. de Thoisy Y. Le Maho M. Brucker N. Châtelain R. Laesser F. Crenner Y. Handrich R. Wilson D. Chevallier Rory Wilson
format	Journal article
container_title	The Journal of Experimental Biology
container_volume	221
container_issue	10
container_start_page	jeb177378
publishDate	2018
institution	Swansea University
issn	0022-0949 1477-9145
doi_str_mv	10.1242/jeb.177378
college_str	Faculty of Science and Engineering
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hierarchy_top_id	facultyofscienceandengineering
hierarchy_top_title	Faculty of Science and Engineering
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department_str	School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
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published_date	2018-05-23T03:51:44Z
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Combined use of two supervised learning algorithms to model sea turtle behaviours from tri-axial acceleration data

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