| Machine learning for vessel trajectories using compression, alignments and domain knowledge GKD de Vries, M Van Someren Expert Systems with Applications, 2012 | 185 | 2012 |
| A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web P Ristoski, GKD De Vries, H Paulheim International semantic web conference, 186-194, 2016 | 143 | 2016 |
| A fast approximation of the Weisfeiler-Lehman graph kernel for RDF data GKD de Vries Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 91 | 2013 |
| Substructure counting graph kernels for machine learning from rdf data GKD De Vries, S De Rooij Journal of Web Semantics 35, 71-84, 2015 | 71 | 2015 |
| Combining ship trajectories and semantics with the simple event model (sem) WR Van Hage, V Malaisé, G de Vries, G Schreiber, M van Someren Proceedings of the 1st ACM International Workshop on Events in Multimedia, 73-80, 2009 | 63 | 2009 |
| An integrated approach for visual analysis of a multisource moving objects knowledge base N Willems, WR van Hage, G de Vries, JHM Janssens, V Malaisé International Journal of Geographical Information Science 24 (10), 1543-1558, 2010 | 62 | 2010 |
| Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM) WR Van Hage, V Malaisé, GKD de Vries, G Schreiber, MW van Someren Multimedia Tools and Applications 57 (1), 175-197, 2012 | 56 | 2012 |
| Clustering vessel trajectories with alignment kernels under trajectory compression G de Vries, M van Someren Joint European Conference on Machine Learning and Knowledge Discovery in …, 2010 | 44 | 2010 |
| A Fast and Simple Graph Kernel for RDF. GKD De Vries, S De Rooij DMoLD 1082, 2013 | 35 | 2013 |
| Comparing vessel trajectories using geographical domain knowledge and alignments GKD de Vries, WR van Hage, M van Someren 2010 IEEE International Conference on Data Mining Workshops, 209-216, 2010 | 27 | 2010 |
| Semi-automatic ontology extension in the maritime domain G de Vries, V Malaisé, M Van Someren, P Adriaans, G Schreiber, A Nijholt, ... Proceedings of the Twentieth Belgian-Dutch Conference on Artificial …, 2008 | 20 | 2008 |
| Machine learning on linked data, a position paper P Bloem, GKD De Vries Linked Data for Knowledge Discovery, 15-19, 2014 | 17 | 2014 |
| Unsupervised ship trajectory modeling and prediction using compression and clustering G de Vries, M van Someren TilburgTilburg centre for Creative Computing (TiCC), Tilburg University, 2009 | 15 | 2009 |
| An analysis of alignment and integral based kernels for machine learning from vessel trajectories GKD De Vries, M Van Someren Expert systems with applications 41 (16), 7596-7607, 2014 | 11 | 2014 |
| Simplifying RDF Data for Graph-Based Machine Learning. P Bloem, A Wibisono, G De Vries KNOW@ LOD, 2014 | 10 | 2014 |
| Kernel methods for vessel trajectories GKD de Vries SIKS, 2012 | 9 | 2012 |
| Learning a model of ship movements R Lagerweij, G de Vries, M van Someren University of Amsterdam, 2009 | 8 | 2009 |
| Hubble: Linked data hub for clinical decision support R Hoekstra, S Magliacane, L Rietveld, G De Vries, A Wibisono, ... Extended Semantic Web Conference, 458-462, 2012 | 7 | 2012 |
| Generating scientific documentation for computational experiments using provenance A Wibisono, P Bloem, GKD de Vries, P Groth, A Belloum, M Bubak International Provenance and Annotation Workshop, 168-179, 2014 | 5 | 2014 |
| Predicting quality of crowdsourced annotations using graph kernels A Nottamkandath, J Oosterman, D Ceolin, GKD de Vries, W Fokkink IFIP International Conference on Trust Management, 134-148, 2015 | 4 | 2015 |
Maarten van SomerenInformatica Instituut, Universiteit van AmsterdamVerified email at uva.nl
