Articles dans des revues avec comité de lecture (20)
  1. 11. Cuellar, M. M., Ros, M., Bautista, M. J. M., Le Borgne, Y.-A., & Bontempi, G. (2015). An approach for the evaluation of human activities in physical therapy scenarios. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 141, 401-414. doi:10.1007/978-3-319-16292-8_29
  2. 12. Dal Pozzolo, A., Le Borgne, Y.-A., Bontempi, G., Caelen, O., & Waterschoot, S. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications, 41(10), 4915-4928. doi:10.1016/j.eswa.2014.02.026
  3. 13. 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 pasly: the ICT4 Rehab project. European journal of paediatric neurology, 17(1), 32.
  4. 14. Bontempi, G., Ben Taieb, S., & Le Borgne, Y.-A. (2013). Machine learning strategies for time series forecasting. Lecture Notes in Business Information Processing, 138 LNBIP, 62-77. doi:10.1007/978-3-642-36318-4_3
  5. 15. Le Borgne, Y.-A., & Bontempi, G. (2012). Time series prediction for energy-efficient wireless sensors: Applications to environmental monitoring and video games. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 102 LNICST, 63-72. doi:10.1007/978-3-642-32778-0_5
  6. 16. Le Borgne, Y.-A., & Campo, A. (2011). Open Review in computer science : Elsevier grand challenge on executable papers. Procedia Computer Science, 4, 778-780. doi:10.1016/j.procs.2011.04.082
  7. 17. Le Borgne, Y.-A., Raybaud, S. S., & Bontempi, G. (2008). Distributed principal component analysis for wireless sensor networks. Sensors, 8(8), 4821-4850. doi:10.3390/s8084821
  8. 18. Miranda, A. A., Le Borgne, Y.-A., & Bontempi, G. (2008). New routes from minimal approximation error to principal components. Neural Processing Letters, 27(3), 197-207. doi:10.1007/s11063-007-9069-2
  9. 19. Le Borgne, Y.-A., Santini, S., & Bontempi, G. (2007). Adaptive model selection for time series prediction in wireless sensor networks. Signal processing, 87(12), 3010-3020. doi:10.1016/j.sigpro.2007.05.015
  10. 20. Le Borgne, Y.-A., Moussaid, M., & Bontempi, G. (2006). Simulation architecture for data processing algorithms in wireless sensor networks. Proceedings (International Conference on Advanced Information Networking and Applications), 2, 1620409, 383-387. doi:10.1109/AINA.2006.311
    11.   Communications publiées lors de congrès ou colloques nationaux et internationaux (13)
  11. 1. 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
  12. 2. 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
    13. << Précédent 1 2 3 4 Suivant >>