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Hillewaere, R., Manderick, B., & Conklin, D.. (In Press). Alignment methods for folk tune classification. Post-proceedings of The 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery. Hildesheim, Germany.
Manderick, B. (1994). AOi\^\^ lJS! ia7-+ T\^ fLfi\^ AX\^\^(Artificial Life; AL). Massively parallel artificial intelligence, 398.
Higuchi, T., Iba, H., & Manderick, B.. (1994). Applying evolvable hardware to autonomous agents. Parallel Problem Solving from Nature—PPSN III, 524–533.
Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowé, A., & Manderick, B.. (2010). Applying subgroup discovery for the analysis of string quartet movements. In Proceedings of 3rd international workshop on Machine learning and music (pp. 29–32). Firenze, Italy: ACM.
Meganck, S., Leray, P., Maes, S., & Manderick, B.. (2006). Apprentissage des réseaux bayésiens causaux à partir de données d’observation et d’expérimentation. Proceedings of 15ème Congrès Francophone Reconnaissance des Formes et Intelligence Artificielle, RFIA, 2006, 131.
Meganck, S., Leray, P., & Manderick, B.. (2009). Causal discovery in non-ideal frameworks. Extended abstracts of the International Multidisciplinary Workshop on Causality.
Meganck, S., Leray, P., & Manderick, B.. (2007). Causal graphical models with latent variables: learning and inference. Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 5–16.
Maes, S., Meganck, S., & Manderick, B.. (2005). Causal inference in multi-agent causal models. Proceedings of Modeles Graphiques Probabilistes pour la Modélisation des Connaissances, Atelier of EGC, 5, 53–62.
Stadler, K., Wellens, P., & De Beule, J.. (2012). The Combinatorial Naming Game. In B. De Baets, Manderick, B., Rademaker, M., & Waegeman, W., Proceedings of the 21st Belgian-Dutch Conference on Machine Learning (BeneLearn 2012). Ghent, Belgium.
PDF icon Stadler 2012 The Combinatorial Naming Game.pdf (1.36 MB)
Opiyo, E. T. O., Ayienga, E., Getao, K., & Manderick, B.. (2006). Computing Research Challenges and Opportunities with Grid Computing. Measuring Computing Research Excellence and Vitality, 112.
Vanschoenwinkel, B., & Manderick, B.. (2005). Context-sensitive kernel functions: A comparison between different context weights. Belgisch Nederlandse Artifcial Intelligence Conference (BNAIC).
Vanschoenwinkel, B., Liu, F., & Manderick, B.. (2006). Context-sensitive kernel functions: A distance function viewpoint. Advances in Machine Learning and Cybernetics, 861–870.
Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B.. (2002). Credit card fraud detection using Bayesian and neural networks. Proceedings of the 1st international naiso congress on neuro fuzzy technologies.
Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowé, A., & Manderick, B.. (2009). Descriptive Mining of Folk Music: A testcase. In Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC 2009).
Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowé, A., & Manderick, B.. (2009). Descriptive subgroup mining of folk music. Second International Workshop on Machine Learning and Music, 1–6.
Van Remortel, P., Ceuppens, J., Defaweux, A., Lenaerts, T., & Manderick, B.. (2003). Developmental effects on tuneable fitness landscapes. In Proceedings of the 5th international conference on Evolvable systems: from biology to hardware (pp. 117–128). Springer-Verlag.