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31st BNAIC / 28th BENELEARN 2019: Brussels, Belgium - Selected Papers
- Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe:
Artificial Intelligence and Machine Learning - 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers. Communications in Computer and Information Science 1196, Springer 2020, ISBN 978-3-030-65153-4
Artificial Intelligence Part: BNAIC
- Jonas Kuckling, Keneth Ubeda Arriaza, Mauro Birattari:
AutoMoDe-IcePop: Automatic Modular Design of Control Software for Robot Swarms Using Simulated Annealing. 3-17 - Gaëtan Spaey, Miquel Kegeleirs, David Garzón-Ramos, Mauro Birattari:
Evaluation of Alternative Exploration Schemes in the Automatic Modular Design of Robot Swarms. 18-33 - Pieter Libin, Nassim Versbraegen, Ana B. Abecasis, Perpetua Gomes, Tom Lenaerts, Ann Nowé:
Towards a Phylogenetic Measure to Quantify HIV Incidence. 34-50 - Wietse de Vries:
Cognitively Plausible Computational Models of Lexical Processing Can Explain Variance in Human Word Predictions and Reading Times. 51-69 - David Winant, Joachim Schreurs, Johan A. K. Suykens:
Latent Space Exploration Using Generative Kernel PCA. 70-82
Machine Learning Part: Benelearn
- Jonathan Peck, Bart Goossens, Yvan Saeys:
Calibrated Multi-probabilistic Prediction as a Defense Against Adversarial Attacks. 85-125 - Mengzi Tang, Raúl Pérez-Fernández, Bernard De Baets:
Machine Learning Methods for Ordinal Classification with Additional Relative Information. 126-136 - Joachim Schreurs, Michaël Fanuel, Johan A. K. Suykens:
Towards Deterministic Diverse Subset Sampling. 137-151 - Alireza Gharahighehi, Celine Vens:
Extended Bayesian Personalized Ranking Based on Consumption Behavior. 152-164 - Joey De Pauw, Sandy Moens, Bart Goethals:
SubSect - An Interactive Itemset Visualization. 165-181 - Théo Verhelst, Olivier Caelen, Jean-Christophe Dewitte, Bertrand Lebichot, Gianluca Bontempi:
Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference. 182-200
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