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Results: 24
Number of items: 24
  • Open Access
    de Vries, G. K. D. (2013). A Fast Approximation of the Weisfeiler-Lehman Graph Kernel for RDF Data. BNAIC, 25, 313. http://bnaic2013.tudelft.nl/proceedings/papers/paper_35.pdf
  • Open Access
    de Vries, G. K. D., & de Rooij, S. (2013). A Fast and Simple Graph Kernel for RDF. 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/paper2.pdf
  • van Hage, W. R., Malaisé, V., de Vries, G. K. D., Schreiber, G., & van Someren, M. W. (2012). Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM). Multimedia Tools and Applications, 57(1), 175-197. https://doi.org/10.1007/s11042-010-0680-2
  • de Vries, G. K. D., & van Someren, M. (2012). Machine learning for vessel trajectories using compression, alignments and domain knowledge. Expert Systems With Applications, 39(18), 13426-13439. https://doi.org/10.1016/j.eswa.2012.05.060
  • Open Access
    de Vries, G. K. D. (2012). Kernel methods for vessel trajectories. [Thesis, fully internal, Universiteit van Amsterdam]. SIKS.
  • de Vries, G. K. D., van Hage, W. R., & van Someren, M. (2011). Comparing vessel trajectories using geographical domain knowledge and alignments. In MAD 2011 Workshop Proceedings: June 17, 2011, Tilburg, The Netherlands (pp. 15-16). Universiteit van Tilburg.
  • de Vries, G. K. D., van Hage, W. R., & van Someren, M. (2011). Comparing vessel trajectories using geographical domain knowledge and alignments. In P. van der Putten, C. Veenman, J. Vanschoren, M. Israel, & H. Blockeel (Eds.), Benelearn 2011: Proceedings of the Twentieth Belgian Dutch Conference on Machine Learning, The Hague, May 20 2011 (pp. 125-126). NFI.
  • de Vries, G., & van Someren, M. (2010). Clustering vessel trajectories with alignment kernels under trajectory compression. In J. L. Balcázar, F. Bonchi, A. Gionis, & M. Sebag (Eds.), Machine Learning and Knowledge Discovery in Databases: European conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010 : proceedings (Vol. 1, pp. 296-311). (Lecture Notes in Computer Science; Vol. 6321), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-642-15880-3_25
  • Willems, N., van Hage, W. R., de Vries, G., Janssens, J. H. M., & Malaisé, V. (2010). An integrated approach for visual analysis of a multisource moving objects knowledge base. International Journal of Geographical Information Science, 24(10), 1543-1558. https://doi.org/10.1080/13658816.2010.515029
  • de Vries, G. K. D., van Hage, W. R., & van Someren, M. (2010). Comparing vessel trajectories using geographical domain knowledge and alignments. In W. Fan, W. Hsu, G. I. Webb, B. Liu, C. Zhang, D. Gunopulos, & X. Wu (Eds.), Proceedings of the 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010), Sydney, Australia (pp. 209-216). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2010.123
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