Toon Van Craenendonck
Toon Van Craenendonck
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Cited by
Using internal validity measures to compare clustering algorithms
T Van Craenendonck, H Blockeel
Benelearn 2015 Poster presentations (online), 1-8, 2015
Constraint-based Clustering Selection
T Van Craenendonck, H Blockeel
Machine Learning, 2018
Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images
N Gerrits, B Elen, TV Craenendonck, D Triantafyllidou, IN Petropoulos, ...
Scientific reports 10 (1), 9432, 2020
Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer’s disease patients
S Lemmens, T Van Craenendonck, J Van Eijgen, L De Groef, R Bruffaerts, ...
Alzheimer's research & therapy 12 (1), 1-13, 2020
wannesm/dtaidistance v2. 0.0
W Meert, K Hendrickx, T Van Craenendonck
Zenodo, 2020
COBRA: A fast and simple method for active clustering with pairwise constraints
T Van Craenendonck, S Dumancic, H Blockeel
International Joint Conference on Artificial Intelligence (IJCAI) 2017, 2017
COBRASTS: A New Approach to Semi-supervised Clustering of Time Series
T Van Craenendonck, W Meert, S Dumančić, H Blockeel
Discovery Science: 21st International Conference, DS 2018, Limassol, Cyprus …, 2018
Hyperspectral imaging and the retina: worth the wave?
S Lemmens, J Van Eijgen, K Van Keer, J Jacob, S Moylett, L De Groef, ...
Translational Vision Science & Technology 9 (9), 9-9, 2020
Cobras: Interactive clustering with pairwise queries
T Van Craenendonck, S Dumančić, E Van Wolputte, H Blockeel
Advances in Intelligent Data Analysis XVII: 17th International Symposium …, 2018
Systematic comparison of heatmapping techniques in deep learning in the context of diabetic retinopathy lesion detection
T Van Craenendonck, B Elen, N Gerrits, P De Boever
Translational vision science & technology 9 (2), 64-64, 2020
COBRAS: fast, iterative, active clustering with pairwise constraints
T Van Craenendonck, S Dumančić, E Van Wolputte, H Blockeel
arXiv preprint arXiv:1803.11060, 2018
Retinal microvascular complexity comparing mono‐and multifractal dimensions in relation to cardiometabolic risk factors in a Middle Eastern population
T Van Craenendonck, N Gerrits, B Buelens, IN Petropoulos, A Shuaib, ...
Acta Ophthalmologica, 2020
Wannesm/Dtaidistance: v2. 3.5
K Wannesm, A Yurtman, P Robberechts, D Vohl, E Ma, G Verbruggen, ...
Zenodo: Genève, Switzerland, 2022
Wannesm/Dtaidistance V1. 1.2
W Meert, T Van Craenendonck
Zenodo, 2018
wannesm/dtaidistance: v2. 3.5
A Yurtman, P Robberechts, D Vohl, E Ma, G Verbruggen, M Rossi, ...
Zenodo, 2021
Tackling noise in active semi-supervised clustering
J Soenen, S Dumančić, T Van Craenendonck, H Blockeel
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020
wannesm/dtaidistance v1. 2.2
T Van Craenendonck, E Ma
Zenodo, 2019
System and method for evaluating a performance of explainability methods used with artificial neural networks
E Bart, N Gerrits, T Van Craenendonck, P De Boever
US Patent App. 17/342,228, 2021
Constraint-based clustering selection
T Van Craenendonck, H Blockeel
Benelearn 2016 Poster Presentations 2016, 1-3, 2016
Limitations of using constraint set utility in semi-supervised clustering
T Van Craenendonck, H Blockeel
Proceedings of the 2015 International Workshop on Meta-Learning and …, 2015
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