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Jean-Michel Begon - Publications ORBI
Begon, J.-M. (2017). A random walk in Machine Learning. Paper presented at Geeks anonymes, Liège, Belgique.
Since the dawn of machine learning (ML), it hasn’t stop spreading into our everyday lives in new, creative ways. Why Google, Facebook, Amazon and the like have invested so much in ML recently? What can (and ...
Begon, J.-M., Joly, A., & Geurts, P. (2017). Globally Induced Forest: A Prepruning Compression Scheme. Proceedings of Machine Learning Research, 70, 420-428.
Peer reviewed
Tree-based ensemble models are heavy memory- wise. An undesired state of affairs consider- ing nowadays datasets, memory-constrained environment and fitting/prediction times. In this paper, we propose the ...
Begon, J.-M., Joly, A., & Geurts, P. (2016, September 12). Joint learning and pruning of decision forests. Paper presented at The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgique.
Peer reviewed
Decision forests such as Random Forests and Extremely randomized trees are state-of-the-art supervised learning methods. Unfortunately, they tend to consume much memory space. In this work, we propose an ...
Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016, January 10). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7.
Peer reviewed (verified by ORBi)
Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of ...
Begon, J.-M. (2014). Generic image classification: random and convolutional approaches. Unpublished master thesis, Université de Liège, ​Liège, ​​Belgique.
Supervised learning introduces genericity in the field of image classification, thus enabling fast progress in the domain. Genericity does not imply ease-of-use, however, and the best methods in term of ...
Begon, J.-M. (2011). Prototypage d'un serveur de données géographiques maillées: Rasdaman. Unpublished master thesis, Université de Liège, ​Liège, ​​Belgique.
In this thesis, we assessed the capability of the Rasdaman software as a full-scale raster data server. In the domain of Geographic Information Systems (GIS), data servers hold an even more prominent position ...