An analysis of alignment and integral based kernels for machine learning from vessel trajectories

Autor: de Vries, Gerben Klaas Dirk, van Someren, Maarten

Publikováno v: In Expert Systems With Applications 15 November 2014 41(16):7596-7607

Predicting Quality of Crowdsourced Annotations using Graph Kernels

Autor: Nottamkandath, Archana, Oosterman, Jasper, de Vries, Gerben Klaas Dirk, Ceolin, Davide, Fokkink, Wan, Murayama, Y., Dimitrakos, T., Jensen, C. D., Marsh, S.

Publikováno v: Nottamkandath, A, Oosterman, J, de Vries, G K D, Ceolin, D & Fokkink, W 2015, Predicting Quality of Crowdsourced Annotations using Graph Kernels . in Y Murayama, T Dimitrakos, C D Jensen & S Marsh (eds), Trust Management IX-9th IFIP Working Group 11.11 International Conference on Trust Management, IFIPTM 2015, Proceedings . vol. 454, IFIP Advances in Information and Communication Technology, vol. 454, Springer New York, pp. 134-148, 9th IFIP Working Group 11.11 International Conference on Trust Management, IFIPTM 2015, Hamburg, Germany, 26/05/15 . https://doi.org/10.1007/978-3-319-18491-3_10
Trust Management IX ISBN: 9783319184906
IFIPTM
IFIP Advances in Information and Communication Technology
9th IFIP International Conference on Trust Management (TM)
9th IFIP International Conference on Trust Management (TM), May 2015, Hamburg, Germany. pp.134-148, ⟨10.1007/978-3-319-18491-3_10⟩
Trust Management IX-9th IFIP Working Group 11.11 International Conference on Trust Management, IFIPTM 2015, Proceedings, 454, 134-148
Trust Management IX: 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015 : proceedings, 134-148
STARTPAGE=134;ENDPAGE=148;TITLE=Trust Management IX

Part 2: Full Papers; International audience; Annotations obtained by Cultural Heritage institutions from the crowd need to be automatically assessed for their quality. Machine learning using graph kernels is an effective technique to use structural i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0b6293be4b84f2bcae0d424d10afc6d
https://doi.org/10.1007/978-3-319-18491-3_10