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A
Abughalieh N, Le Borgne Y, Nowé A, Steenhaut K. Lifetime Optimization for Sensor Networks with Correlated Data Gathering. In: Altman E, Basar T, Chlamtac I Proceedings of the 7th IEEE Conference on Networked Sensing Systems. Proceedings of the 7th IEEE Conference on Networked Sensing Systems. ; 2010. p. 252 – 258.
Ampe EM, Hestir EL, Bresciani M, Salvadore E, Brando VE, Dekker A, Malthus TJ, Jansen M, Triest L, Batelaan O, et al. A wavelet approach for estimating chlorophyll-a from inland waters. IEEE Geoscience and Remote Sensing Letters. 2014:89-93.
Ampe EM, Raymaekers D, Hestir EL, Jansen M, Knaeps E, Batelaan O. A wavelet enhanced semi-analytical inversion model for water quality retrieval from high spectral resolution data for complex waters. IEEE Transactions on Geoscience and Remote Sensing [Internet]. 2015:869-882.
B
Baeten L, Reumers J, Tur V, c}ois Stricher F{\c, Lenaerts T, Serrano L, Rousseau F, Schymkowitz JWH. Reconstruction of protein backbones from the BriX collection of canonical protein fragments. PLoS Computational Biology [Internet]. 2008:e1000083.
Bejjani G, Caelen O, Bontempi G, Perrin L, Barvais L. Retrospective comparison of manual versus semi-automated propofol-remifentanil TCI Anaesthesia (abstract).
Ben Taieb S, Hyndman R. Boosting multi-step autoregressive forecasts.
Ben Taieb S, Bontempi G, Atiya A, Sorjamaa A. A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert systems with applications. 2012:7067–7083.
Ben Taieb S, Bontempi G. Recursive multi-step time series forecasting by perturbing data.
Ben Taieb S, J Hyndman R. A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting [Internet]. 2013:1–19.
Ben Taieb S, J Hyndman R. Recursive and direct multi-step forecasting : the best of both worlds.
Ben Taieb S. Machine learning strategies for multi-step-ahead time series forecasting.
Bersini H, Lenaerts T, Santos FC. Growing biological networks: beyond the gene-duplication model. Journal of Theoretical Biology [Internet]. 2006:488–505.
Bini DA, Dendievel S, Guy Latouche, Meini B. Computing the exponential of large block-triangular block-Toeplitz matrices encountered in fluid queues.
Bonnechere B, Wermenbol V, Dan B, Salvia P, Borgne Y-AL, Bontempi G, Vansummeren S, Sholukha V, Moiseev F, Jansen B, et al. Management and interpretation of medical data related to cerebral pasly: the ICT4 Rehab project. European journal of paediatric neurology. 2013:32.
Bontempi G, Vaccaro A, Villacci D. Power cables thermal protection by interval simulation of imprecise dynamical systems. IEE Proceedings of Generation Transmission and Distribution. 2004.
Bontempi G, Meyer PE. Causal filter selection in microarray data. ICML'10, International Conference of Machine Learning [Internet]. 2010.
Bontempi G, Meyer PE. A model-based relevance estimation approach for feature selection in microarray datasets. In: Proceedings of ICANN 2008. Vol. II. Proceedings of ICANN 2008. ; 2008. p. 21-31.
Bontempi G, Birattari M, Bersini H. Lazy learning for modeling and control design. International Journal of Control. 1999:643-658.
Bontempi G, Birattari M. From Linearization to Lazy Learning: A Survey of Divide-and-Conquer Techniques for Nonlinear Control. International Journal of Computational Cognition. 2005:56-73.
Bontempi G, Kruijtzer W. The use of intelligent data analysis techniques for system-level design: a software estimation example. Soft Computing Journal. 2004:477-490.
Bontempi G, Le Borgne Y. An Adaptive Modular Approach to the Mining of Sensor Network Data.
Bontempi G. A blocking strategy to improve gene selection for classification of gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2007:293-300.
Bontempi G. Structural feature selection for wrapper methods.
Bontempi G. Long Term Time Series Prediction with Multi-Input Multi-Output Local Learning.
Bontempi G, Le Borgne Y. An Adaptive Modular Approach to the Mining of Sensor Network Data.

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