Ouvrages publiés à titre de seul auteur (1)

  1. 1. Bontempi, G. (2021). Statistical foundations of machine learning.
  2.   Ouvrages publiés en collaboration (2)

  3. 1. Bontempi, G., & Levy, E. (2008). Modélisation et simulation. Bruxelles: PUB Presses Universitaires de Bruxelles.
  4. 2. Bontempi, G., Da Silva Soares, A., & De Wulf, M. (2005). Calcul formel et numérique. Bruxelles: Presses universitaires de Bruxelles.
  5.   Ouvrages édités à titre de seul éditeur ou en collaboration (1)

  6. 1. Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., & Louppe, G. (2021). Artificial Intelligence and Machine Learning: 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers. doi:10.1007/978-3-030-65154-1
  7.   Parties d'ouvrages collectifs (12)

  8. 1. Dal Pozzolo, A., Caelen, O., & Bontempi, G. (2015). When is undersampling effective in unbalanced classification tasks? In Machine Learning and Knowledge Discovery in Databases. Springer.
  9. 2. Meyer, P. E., & Bontempi, G. (2014). Information-theoretic gene selection in expression data. In Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data (pp. 399-419). wiley. doi:10.1002/9781118617151.ch17
  10. 3. Olsen, C., Haibe-Kains, B., Quackenbush, J., & Bontempi, G. (2013). On the Integration of Prior Knowledge in the Inference of Regulatory Networks. In On the Integration of Prior Knowledge in the Inference of Regulatory Networks.. World Scientific.(Biological Data Mining and Its Applications in Healthcare).
  11. 4. Le Borgne, Y.-A., & Bontempi, G. (2012). Prediction-Based Data Collection in Wireless Sensor Networks. In Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning (1 ed., pp. 153-180). Boca Raton: CRC Press. doi:10.1201/b14300
  12. 5. Meyer, P. E., Olsen, C., & Bontempi, G. (2011). Transcriptional Network Inference based on Information Theory. In Transcriptional Network Inference based on Information Theory.. Wiley-VCH.(Applied Statistics for Network Biology: Methods in Systems Biology.). doi:10.1002/9783527638079.ch4
  13. 6. Miranda, A. A., Caelen, O., & Bontempi, G. (2009). Machine Learning for Automated Polyp Detection in Computed Tomography Colonography. In F. A. Gonzalez & E. Romero (Eds.), Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques (pp. 54-77). Medical information science reference.
  14. 7. Dricot, J.-M., Bontempi, G., & De Doncker, P. (2009). Static and dynamic localization techniques. In G. G. F. Ferrari (Ed.), Sensor networks: where theory meets practice.
  15. 8. Dricot, J.-M., Bontempi, G., & De Doncker, P. (2009). Static and dynamic localization techniques for wireless sensor networks. In Static and dynamic localization techniques for wireless sensor networks: where theory meets practice (pp. 249-281). Springer.(Sensor Networks).

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