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2020 – today
- 2024
[c21]Fabian Fingerhut
, Mathias Verbeke, Elena Tsiporkova:
Unsupervised context-sensitive anomaly detection on streaming data relying on multi-view profiling. EAIS 2024: 1-10- [c20]Louis Carpentier, Arne De Temmerman, Mathias Verbeke:
Towards Contextual, Cost-Efficient Predictive Maintenance in Heavy-Duty Trucks. IDA (2) 2024: 260-267 - [c19]Louis Carpentier, Len Feremans, Wannes Meert, Mathias Verbeke:
Pattern-based Time Series Semantic Segmentation with Gradual State Transitions. SDM 2024: 316-324 - 2023
- [c18]Fabian Fingerhut, Sarah Klein, Mathias Verbeke, Sreeraj Rajendran, Elena Tsiporkova:
Multi-view contextual performance profiling in rotating machinery. ICPHM 2023: 343-350 - [c17]Arne De Temmerman, Matthias De Ryck, Tom Hellemans, Mathias Verbeke:
Infrared hyperspectral analysis for non-invasive, inline fat content determination in bakery products. INDIN 2023: 1-7 - 2022
- [c16]Tiberius-George Sorescu, Chandrakanth R. Kancharla, Jeroen Boydens, Hans Hallez, Mathias Verbeke:
Framework to Evaluate Deep Learning Algorithms for Edge Inference and Training. PKDD/ECML Workshops (1) 2022: 569-581 - 2020
- [j1]Jonas Maeyens, Annemie Vorstermans, Mathias Verbeke:
Process mining on machine event logs for profiling abnormal behaviour and root cause analysis. Ann. des Télécommunications 75(9-10): 563-572 (2020) - [c15]Sarah Klein, Mathias Verbeke:
An unsupervised methodology for online drift detection in multivariate industrial datasets. ICDM (Workshops) 2020: 392-399
2010 – 2019
- 2019
- [c14]Mathias Van Herreweghe, Mathias Verbeke, Wannes Meert, Tom Jacobs:
A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes. BNAIC/BENELEARN 2019 - [c13]Mathias Van Herreweghe, Mathias Verbeke, Wannes Meert, Tom Jacobs:
A Machine Learning-Based Approach for Predicting Tool Wear in Industrial Milling Processes. PKDD/ECML Workshops (2) 2019: 414-425 - 2016
- [c12]Christiaan Leysen, Mathias Verbeke, Pierre Dagnely, Wannes Meert:
Energy consumption profiling using Gaussian processes. IEEE Conf. on Intelligent Systems 2016: 470-477 - 2015
- [c11]Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke:
Inducing Probabilistic Relational Rules from Probabilistic Examples. IJCAI 2015: 1835-1843 - 2014
- [c10]Mathias Verbeke, Paolo Frasconi, Kurt De Grave, Fabrizio Costa, Luc De Raedt:
kLogNLP: Graph Kernel-based Relational Learning of Natural Language. ACL (System Demonstrations) 2014: 85-90 - [c9]Fabrizio Costa, Mathias Verbeke, Luc De Raedt:
Relational Regularization and Feature Ranking. SDM 2014: 650-658 - [c8]Willem Mattelaer, Mathias Verbeke, Davide Nitti:
KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context. SemEval@COLING 2014: 385-390 - [c7]Mathias Verbeke, Vincent Van Asch, Walter Daelemans, Luc De Raedt:
Lazy and Eager Relational Learning Using Graph-Kernels. SLSP 2014: 171-184 - 2013
- [c6]Guilherme de Oliveira da Costa Marques, Mathias Verbeke:
Combining Text Mining Techniques for QA4MRE 2013. CLEF (Working Notes) 2013 - 2012
- [c5]Mathias Verbeke, Jesse Davis:
A Text Mining Approach as Baseline for QA4MRE'12. CLEF (Online Working Notes/Labs/Workshop) 2012 - [c4]Mathias Verbeke, Vincent Van Asch, Roser Morante, Paolo Frasconi, Walter Daelemans, Luc De Raedt:
A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories. EMNLP-CoNLL 2012: 579-589 - [c3]Mathias Verbeke, Ilija Subasic, Bettina Berendt:
How to Carve up the World: Learning and Collaboration for Structure Recommendation. MSM/MUSE 2012: 1-22 - 2011
- [c2]Mathias Verbeke, Paolo Frasconi, Vincent Van Asch, Roser Morante, Walter Daelemans, Luc De Raedt:
Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection. ILP 2011: 347-357
2000 – 2009
- 2009
- [c1]Mathias Verbeke, Bettina Berendt, Siegfried Nijssen:
Data Mining, Interactive Semantic Structuring, and Collaboration: A Diversity-aware Method for Sense-making in Search. LivingWeb@ISWC 2009
Coauthor Index
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