Communications publiées lors de congrès ou colloques nationaux et internationaux (39)
  1. 14. Safin, S., Defays, A., Billon, A., Decaestecker, C., & Warzée, N. (2009). Influence d’images évocatrices et distractrices sur une tâche de jugement en acoustique des salles. In IHM 2009, ACM International Conference Proceeding Series (pp. 241-248) ACM.
  2. 15. Debeir, O., Adanja, I., Warzée, N., Van Ham, P., & Decaestecker, C. (2008). Phase contrast image segmentation by weak watershed transform assembly. In Proceedings of 5th IEEE International Symposium on Biomedical Imaging : From Nano to Macro (ISBI) (pp. 724-727) IEEE.
  3. 16. Yen, L., Fouss, F., Decaestecker, C., Francq, P., & Saerens, M. (2007). Graph Nodes Clustering based on the Commute-Time Kernel. In Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007): Lecture Notes in Computer Science: Vol. Vol. LNAI4426 (pp. pp. 1037-1045) Springer-Verlag.
  4. 17. Debeir, O., Milojevic, D., Leloup, T., Van Ham, P., Kiss, R., & Decaestecker, C. (2005). Mitotic Tree Construction by Computer In Vitro Cell Tracking : a Tool for Proliferation and Motility Features Extraction. In Proc. of the EUROCON 2005 (pp. 951-954) IEEE, Piscataway.
  5. 18. Lyazghi, A., Decaestecker, C., Camby, I., Kiss, R., & Van Ham, P. (2001). Characterization of acting filaments in cancer cells by the Hough transform. In Proceedings of the 2001 IASTED International Conference on Signal Processing, Pattern Recognition and Applications (pp. pp. 138-142) M.H. Hamza (ed.).
  6. 19. Latinne, P., Saerens, M., & Decaestecker, C. (2001). Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001) (pp. pp 298-305). (C.E. Brodley and A. Pohoreckyj Danyluk (eds)). Morgan Kaufmann.
  7. 20. Latinne, P., Debeir, O., & Decaestecker, C. (2001). Limiting the Number of Trees in Random Forests. In Multiple Classifier Systems: Proceedings of the Second International Workshop (pp. 178-187). (Lecture Notes in Computer Science). Springer.
  8. 21. Latinne, P., Debeir, O., & Decaestecker, C. (2000). Mixing Bagging and Multiple Feature Subsets to Improve Classification Accuracy of Decision Tree Combination. In BENELEARN 2000, Tenth Belgian-Dutch Conference on Machine Learning. ((8 p.)). .
  9. 22. Latinne, P., Debeir, O., & Decaestecker, C. (2000). Different Ways of Weakening Decision Trees and Their Impact on Classification Accuracy of DT Combination. In Multiple Classifier Systems: Proceedings of the First International Workshop: Vol. 1857 (pp. 200-209). (Lecture Notes in Computer Science, Springer). G. Goos, J. Hartmanis and J. van Leeuwen.
  10. 23. Bersini, H., Bontempi, G., & Decaestecker, C. (1995). Towards Neuro-Fuzzy Defuzzification in Benelearn '95. In Proceedings of the 5th Belgian-Dutch Conference on Machine Learning (pp. 91-98) .
  11. 24. Decaestecker, C., & Saerens, M. (1995). Comparisons of different RBF networks for pattern classification. In F. Fogelman-Soulié & P. Gallinary (Eds.), Industrial applications of neural networks (pp. 591-596) Singapore: World Scientific.
  12. 25. Bersini, H., Bontempi, G., & Decaestecker, C. (1995). Comparing RBF and Fuzzy Inference Systems on theoretical and practical basis. In Proc. ICANN'95 (Int. Conf. on Artificial Neural Networks) (pp. pp 169-174). (EC2 & Cie). F. Fogelman-Soulié and P. Gallinary (eds).
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