Search results
Results: 24
Number of items: 24
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Hoekstra, R., Magliacane, S., Rietveld, L., de Vries, G., Wibisono, A., & Schlobach, S. (2015). Hubble: Linked Data Hub for Clinical Decision Support. In E. Simperl, B. Norton, D. Mladenic, E. Della Valle, I. Fundulaki, A. Passant, & R. Troncy (Eds.), The Semantic Web: ESWC 2012 Satellite Events: ESWC 2012 Satellite Events, Heraklion, Crete, Greece, May 27-31, 2012 : revised selected papers (pp. 458-462). (Lecture Notes in Computer Science; Vol. 7540). Springer. https://doi.org/10.1007/978-3-662-46641-4_45
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Wibisono, A., Bloem, P., de Vries, G. K. D., Groth, P., Belloum, A., & Bubak, M. (2015). Generating scientific documentation for computational experiments using provenance. In B. Ludäscher, & B. Plale (Eds.), Provenance and Annotation of Data and Processes: 5th International Provenance and Annotation Workshop, IPAW 2014, Cologne, Germany, June 9-13, 2014 : revised selected papers (pp. 168-179). (Lecture Notes in Computer Science; Vol. 8628). Springer. https://doi.org/10.1007/978-3-319-16462-5_13
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Nottamkandath, A., Oosterman, J., Ceolin, D., de Vries, G. K. D., & Fokkink, W. (2015). Predicting Quality of Crowdsourced Annotations Using Graph Kernels. In C. Damsgaard Jensen, S. Marsh, T. Dimitrakos, & Y. Murayama (Eds.), Trust Management IX: 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015 : proceedings (pp. 134-148). (IFIP Advances in Information and Communication Technology; Vol. 454). Springer. https://doi.org/10.1007/978-3-319-18491-3_10
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de Vries, G. K. D., & de Rooij, S. (2015). Substructure counting graph kernels for machine learning from RDF data. Journal of Web Semantics, 35(2), 71-84. https://doi.org/10.1016/j.websem.2015.08.002
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de Vries, G. K. D., & van Someren, M. (2014). An analysis of alignment and integral based kernels for machine learning from vessel trajectories. Expert Systems With Applications, 41(16), 7596-7607. https://doi.org/10.1016/j.eswa.2014.05.025
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Bloem, P., & de Vries, G. K. D. (2014). Machine Learning on Linked Data, a Position Paper. In I. Tiddi, M. d'Aquin, & N. Jay (Eds.), Proceedings of the 1st Workshop on Linked Data for Knowledge Discovery: co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014) : Nancy, France, September 19th, 2014 (pp. 69-73). (CEUR Workshop Proceedings; Vol. 1232). CEUR-WS. http://ceur-ws.org/Vol-1232/paper7.pdf -
de Vries, G. K. D. (2013). A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data. In H. Blockeel, K. Kersting, S. Nijssen, & F. Železný (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013: proceedings (Vol. 1, pp. 606-621). (Lecture Notes in Computer Science; Vol. 8188), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-642-40988-2_39
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de Vries, G., & van Someren, M. (2013). Recognizing Vessel Movements from Historical Data. In P. van de Laar, J. Tretmans, & M. Borth (Eds.), Situation awareness with systems of systems (pp. 105-118). Springer. https://doi.org/10.1007/978-1-4614-6230-9_7
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de Vries, G. K. D. (2013). Graph Kernels for Task 1 and 2 of the Linked Data Data-Mining Challenge 2013. In C. d'Amato, P. Berka, V. Svátek, & K. Wecel (Eds.), Proceedings of the International Workshop on Data Mining on Linked Data, with Linked Data Mining Challenge: collocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013) : Prague, Czech Republic, September 23, 2013 (CEUR Workshop Proceedings; Vol. 1082). CEUR-WS. http://ceur-ws.org/Vol-1082/paper3.pdf
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