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Gerben de Vries
Gerben de Vries
Applied Scientist at Wizenoze.com
Verified email at wizenoze.com
Title
Cited by
Cited by
Year
Machine learning for vessel trajectories using compression, alignments and domain knowledge
GKD de Vries, M Van Someren
Expert Systems with Applications, 2012
1592012
A collection of benchmark datasets for systematic evaluations of machine learning on the semantic web
P Ristoski, GKD De Vries, H Paulheim
The Semantic Web–ISWC 2016: 15th International Semantic Web Conference, Kobe†…, 2016
1192016
A fast approximation of the Weisfeiler-Lehman graph kernel for RDF data
GKD de Vries
Machine Learning and Knowledge Discovery in Databases: European Conference†…, 2013
802013
Substructure counting graph kernels for machine learning from rdf data
GKD De Vries, S De Rooij
Journal of Web Semantics 35, 71-84, 2015
632015
An integrated approach for visual analysis of a multisource moving objects knowledge base
N Willems, WR van Hage, G de Vries, JHM Janssens, V Malaisť
International Journal of Geographical Information Science 24 (10), 1543-1558, 2010
612010
Combining ship trajectories and semantics with the simple event model (sem)
WR van Hage, V Malaisť, G de Vries, G Schreiber, M van Someren
Proceedings of the 1st ACM International Workshop on Events in Multimedia, 73-80, 2009
602009
Abstracting and reasoning over ship trajectories and web data with the Simple Event Model (SEM)
WR Van Hage, V Malaisť, GKD de Vries, G Schreiber, MW van Someren
Multimedia Tools and Applications 57, 175-197, 2012
512012
Clustering vessel trajectories with alignment kernels under trajectory compression
G de Vries, M van Someren
Machine Learning and Knowledge Discovery in Databases: European Conference†…, 2010
432010
A Fast and Simple Graph Kernel for RDF.
GKD De Vries, S De Rooij
DMoLD 1082, 2013
322013
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
262010
Semi-automatic ontology extension in the maritime domain
G de Vries, V Malaisť, M Van Someren, P Adriaans, G Schreiber, A Nijholt, ...
Proceedings of the Twentieth Belgian-Dutch Conference on Artificial†…, 2008
172008
Machine learning on linked data, a position paper
P Bloem, GKD De Vries
Linked Data for Knowledge Discovery, 15-19, 2014
162014
Unsupervised ship trajectory modeling and prediction using compression and clustering
G de Vries, M van Someren
Proc. BeneLearn, 7-12, 2009
142009
Simplifying RDF Data for Graph-Based Machine Learning.
P Bloem, A Wibisono, G De Vries
KNOW@ LOD, 2014
112014
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
102014
Hubble: Linked data hub for clinical decision support
R Hoekstra, S Magliacane, L Rietveld, G De Vries, A Wibisono, ...
The Semantic Web: ESWC 2012 Satellite Events: ESWC 2012 Satellite Events†…, 2015
82015
Kernel methods for vessel trajectories
GKD de Vries
SIKS, 2012
82012
Learning a model of ship movements
R Lagerweij, G de Vries, M van Someren
University of Amsterdam, 2009
82009
Generating scientific documentation for computational experiments using provenance
A Wibisono, P Bloem, GKD de Vries, P Groth, A Belloum, M Bubak
Provenance and Annotation of Data and Processes: 5th International†…, 2015
62015
Predicting quality of crowdsourced annotations using graph kernels
A Nottamkandath, J Oosterman, D Ceolin, GKD de Vries, W Fokkink
Trust Management IX: 9th IFIP WG 11.11 International Conference, IFIPTM 2015†…, 2015
42015
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