Kurt De Grave
Katholieke Universiteit Leuven
32 Papers
74 Citations
Kurt De Grave is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Graph kernel & Metagenomics. The author has an hindex of 10, co-authored 29 publications. Previous affiliations of Kurt De Grave include Ghent University & University of Copenhagen Faculty of Science.
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Papers
Multi-objective optimization with surrogate trees
Denny Verbeeck,Francis Maes,Kurt De Grave,Hendrik Blockeel +3 more
- 06 Jul 2013
TL;DR: The use of model trees are investigated as an alternative kind of model providing a good compromise between high expressiveness and low training time, and the empirical results show the promise of the approach for problems on which classical surrogate-based optimizers are painfully slow.
kLog: A language for logical and relational learning with kernels
TL;DR: KLog as discussed by the authors is a kernel-based approach to statistical relational learning, which allows users to specify learning problems declaratively and allows access by the kernel to the rich representation mediated by a technique called graphicalization.
Machine learning applications in proteomics research: How the past can boost the future
Pieter Kelchtermans,Pieter Kelchtermans,Wout Bittremieux,Kurt De Grave,Sven Degroeve,Jan Ramon,Kris Laukens,Dirk Valkenborg,Dirk Valkenborg,Dirk Valkenborg,Harald Barsnes,Lennart Martens +11 more
TL;DR: An overview of the different applications of machine learning in proteomics that together cover nearly the entire wet‐ and dry‐lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis are presented.
Reference dataset and benchmark for reconstructing laser parameters from on-axis video in powder bed fusion of bulk stainless steel
Ayyoub Ahar,Kurt De Grave +1 more
TL;DR: In this paper , the authors present RAISE-LPBF, a large dataset on the effect of laser power and laser dot speed in powder bed fusion (LPBF) of 316L stainless steel bulk material, monitored by on-axis 20k FPS video.
Recovery of gene haplotypes from a metagenome
Samuel M. Nicholls,Wayne Aubrey,Arwyn Edwards,Kurt De Grave,Sharon Huws,Schietgat Leander,Andre Soares,Christopher J. Creevey,Amanda Clare +8 more
TL;DR: Hansel and Gretel: a freely-available data structure and algorithm, providing a software package that reconstructs the most likely haplotypes from metagenomes, and it is shown that Gretel’s haplotypes can be analyzed to determine a significant difference in mutation rates between core and accessory gene families in an ovine rumen microbiome.