Communications publiées lors de congrès ou colloques nationaux et internationaux (67)
  1. 21. Meyer, P. E., Haibe-Kains, B., & Bontempi, G. (2009). Meta-Analysis of Transcriptional Network Inference. In Recomb Satellite 09 MIT.
  2. 22. Ben Taieb, S., Bontempi, G., Sorjamaa, A., & Lendasse, A. (2009). Long-Term Prediction of Time Series by combining Direct and MIMO Strategies. Proceedings of the 2009 IEEE International Joint Conference on Neural Networks (pp. 3054-3061) Internationl Joint Conference on Neural Networks(Atlanta).
  3. 23. Dricot, J.-M., Van Der Haegen, M., Le Borgne, Y.-A., & Bontempi, G. (2008). A modular framework for user localization and tracking using machine learning techniques in wireless sensor networks. Proc. of the 7th IEEE Conference on Sensors, IEEE Sensors (26-29 October, 2008: Leece, Italy)
  4. 24. Dricot, J.-M., Van Der Haegen, M., Le Borgne, Y.-A., & Bontempi, G. (2008). Performance evaluation of machine learning techniques for the localization of users in wireless sensor networks. Proc. of the annual machine learning conference of Belgium and The Netherlands, Benelearn (19-20 May, 2008: Spa, Belgium)
  5. 25. Olsen, C., Meyer, P. E., & Bontempi, G. (2008). Fact sheet: Using mutual information to infer causal relationships Catharina. JMLR: Workshop and Conference Proceedings - NIPS 2008 workshop on causality
  6. 26. Bontempi, G. (2008). Long Term Time Series Prediction with Multi-Input Multi-Output Local Learning. Proceedings of the 2nd European Symposium on Time Series Prediction (TSP) ESTSP08
  7. 27. Kontos, K., & Bontempi, G. (2006). Scale-free Paradigm in Yeast Genetic Regulatory Network inferred from microarray data. Proceedings of AISB'06: Adaptation in Artificial and Biological Systems. Vol. 3 (pp. 139-144).
  8. 28. Caelen, O., & Bontempi, G. (2005). How to allocate a restricted budget of leave-one-out assessments for effective model selection in machine learning: a comparison of state-of-the-art techniques. In A. Nowe (Ed.), Proceedings of the 17th Belgian-Dutch Conference on Artificial Intelligence (pp. 51-58) KVAB.
  9. 29. Le Borgne, Y.-A., & Bontempi, G. (2005). Round robin cycle for predictions in wireless sensor networks. Proceedings of the 2nd IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (pp. 253-258) (5-8 December 2005).
  10. 30. Bontempi, G., & Le Borgne, Y.-A. (2005). An adaptive modular approach to the mining of sensor network data. Siam Data Mining 2005 workshop on "Data mining in sensor networks"
  11. 31. Bontempi, G. (2005). Structural feature selection for wrapper methods. Proceedings of ESANN 2005 European Symposium on Artificial Neural Networks(Bruges, Belgium)
  12. 32. Bontempi, G. (2005). On the use of feature selection to deal with the curse of dimensionality in microarray datasets. VIB Microarray Usergroup Meeting (16-18 November 2005: Gent, Belgium)
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