Mark Hoogendoorn
Mark Hoogendoorn
Full Professor of Artificial Intelligence, Vrije Universiteit Amsterdam
Verified email at - Homepage
Cited by
Cited by
Parameter control in evolutionary algorithms: Trends and challenges
G Karafotias, M Hoogendoorn, ÁE Eiben
IEEE Transactions on Evolutionary Computation 19 (2), 167-187, 2014
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
LM Fleuren, TLT Klausch, CL Zwager, LJ Schoonmade, T Guo, ...
Intensive care medicine 46, 383-400, 2020
Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions
T Bosse, M Hoogendoorn, MCA Klein, J Treur, CN Van Der Wal, ...
Autonomous Agents and Multi-Agent Systems 27, 52-84, 2013
Ckconv: Continuous kernel convolution for sequential data
DW Romero, A Kuzina, EJ Bekkers, JM Tomczak, M Hoogendoorn
arXiv preprint arXiv:2102.02611, 2021
Attentive group equivariant convolutional networks
D Romero, E Bekkers, J Tomczak, M Hoogendoorn
International Conference on Machine Learning, 8188-8199, 2020
Flexconv: Continuous kernel convolutions with differentiable kernel sizes
DW Romero, RJ Bruintjes, JM Tomczak, EJ Bekkers, M Hoogendoorn, ...
arXiv preprint arXiv:2110.08059, 2021
The triangle of life: Evolving robots in real-time and real-space
AE Eiben, N Bredeche, M Hoogendoorn, J Stradner, J Timmis, A Tyrrell, ...
European conference on artificial life (ECAL-2013), 1-8, 2013
Modeling centralized organization of organizational change
M Hoogendoorn, CM Jonker, MC Schut, J Treur
Computational and Mathematical Organization Theory 13, 147-184, 2007
Deep learning-based energy disaggregation and on/off detection of household appliances
J Jiang, Q Kong, MD Plumbley, N Gilbert, M Hoogendoorn, DM Roijers
ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (3), 1-21, 2021
Machine learning for the quantified self
M Hoogendoorn, B Funk
On the art of learning from sensory data, 2018
Formal modelling and comparing of disaster plans
M Hoogedoorn, C Jonker, V Popova, A Sharpanskykh, L Xu
Predicting social anxiety treatment outcome based on therapeutic email conversations
M Hoogendoorn, T Berger, A Schulz, T Stolz, P Szolovits
IEEE journal of biomedical and health informatics 21 (5), 1449-1459, 2016
Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records
R Kop, M Hoogendoorn, A Ten Teije, FL Büchner, P Slottje, LMG Moons, ...
Computers in biology and medicine 76, 30-38, 2016
Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer
M Hoogendoorn, P Szolovits, LMG Moons, ME Numans
Artificial intelligence in medicine 69, 53-61, 2016
Generic parameter control with reinforcement learning
G Karafotias, AE Eiben, M Hoogendoorn
Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014
Agent-based analysis of patterns in crowd behaviour involving contagion of mental states
T Bosse, M Hoogendoorn, MCA Klein, J Treur, CN Van Der Wal
Modern Approaches in Applied Intelligence: 24th International Conference on …, 2011
Modeling situation awareness in human-like agents using mental models
M Hoogendoorn, RM van Lambalgen, J Treur
Twenty-Second International Joint Conference on Artificial Intelligence, 2011
Modeling the dynamics of mood and depression
F Both, M Hoogendoorn, M Klein, J Treur
ECAI 2008, 266-270, 2008
Predictive modeling in e-mental health: a common language framework
D Becker, W van Breda, B Funk, M Hoogendoorn, J Ruwaard, H Riper
Internet interventions 12, 57-67, 2018
Reinforcement learning for personalization: A systematic literature review
F Den Hengst, EM Grua, A el Hassouni, M Hoogendoorn
Data Science 3 (2), 107-147, 2020
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