Articles dans des revues avec comité de lecture (156)
  1. 153. Bontempi, G., Birattari, M., & Bersini, H. (1998). Recursive lazy learning for modeling and control. Lecture notes in computer science, 1398, 292-303.
  2. 154. Bersini, H., & Bontempi, G. (1997). Now comes the time to defuzzify neuro-fuzzy models. Fuzzy sets and systems, 90(2), 161-169. doi:10.1016/S0165-0114(97)00082-1
  3. 155. Bonarini, A., & Bontempi, G. (1994). A qualitative simulation approach for fuzzy dynamical models. ACM transactions on modeling and computer simulation, 4(4), 285-313. doi:10.1145/200883.200884
  4. 156. Bonarini, A., & Bontempi, G. (1994). Qualitative simulation of approximate models: an approach based on fuzzy sets. Progress in cybernetics and systems research, 359-366.
    5.   Communications publiées lors de congrès ou colloques nationaux et internationaux (67)
  5. 1. Simar, C., Petieau, M., Cebolla Alvarez, A. M., Leroy, A., Bontempi, G., & Chéron, G. (2020). EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband. doi:10.1109/IJCNN48605.2020.9207486
  6. 2. De Stefani, J., Caelen, O., Hattab, D., Le Borgne, Y.-A., & Bontempi, G. (2019). A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting. In ECML PKDD 2018 Workshops. (Lecture Notes in Computer Science, 11054, 11054). Springer. doi:10.1007/978-3-030-13463-1_1
  7. 3. Bontempi, G., Le Borgne, Y.-A., & De Stefani, J. (2017). A Dynamic Factor Machine Learning Method for Multi-variate and Multi-step-Ahead Forecasting. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 222-231). doi:10.1109/DSAA.2017.1
  8. 4. De Stefani, J., Caelen, O., Hattab, D., & Bontempi, G. (2017). Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies. 2nd Workshop on MIning DAta for financial applicationS (MIDAS). Vol. 1941 (pp. 17-28) MIDAS 2017(Skopje, Macedonia).
  9. 5. Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015). Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information. Neural Networks (IJCNN), 2015 International Joint Conference on doi:10.1109/IJCNN.2015.7280527
  10. 6. Dal Pozzolo, A., Caelen, O., Johnson, R., & Bontempi, G. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium on Computational Intelligence and Data Mining
  11. 7. Dal Pozzolo, A., Johnson, R., Caelen, O., Waterschoot, S., Chawla, N. V., & Bontempi, G. (2014). Using HDDT to avoid instances propagation in unbalanced and evolving data streams. Neural Networks (IJCNN), 2014 International Joint Conference on (pp. 588-594). doi:10.1109/IJCNN.2014.6889638
  12. 8. Bonnechere, B., Wermenbol, V., Dan, B., Salvia, P., Le Borgne, Y.-A., Bontempi, G., Vansummeren, S., Sholukha, V., Moiseev, F., Jansen, B., Rooze, M., & Van Sint Jan, S. (2013). Management and interpretation of medical data related to Cerebral Palsy : the ICT4Rehab project. EPNS (2013: Brussels, Belgium)
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