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ORCIDJos De Brabanter
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2010 – 2019
- 2019
[c10]Philippe Saey, Steven De Wilde, Annemarie M. Kökösy, Jos Knockaert, Dimitri De Schuyter, Jos De Brabanter:
Using code generated by MATLAB for the Mold Level Control System of a Continuous Slab Caster in ArcelorMittal Gent. ETFA 2019: 1497-1500- 2014
- [j11]Jeroen Wyffels, Jos De Brabanter, Pieter Crombez, Piet Verhoeve, Bart Nauwelaers
, Lieven De Strycker:
Distributed, Signal Strength-Based Indoor Localization Algorithm for Use in Healthcare Environments. IEEE J. Biomed. Health Informatics 18(6): 1887-1893 (2014) - 2013
- [j10]Kris De Brabanter, Jos De Brabanter, Bart De Moor, Irène Gijbels:
Derivative estimation with local polynomial fitting. J. Mach. Learn. Res. 14(1): 281-301 (2013) - [c9]Bart Huyck, Jos De Brabanter, Bart De Moor, Jan F. M. Van Impe, Filip Logist:
Model predictive control of a pilot-scale distillation column using a programmable automation controller. ECC 2013: 1053-1058 - 2012
- [j9]Kris De Brabanter, Peter Karsmakers, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Confidence bands for least squares support vector machine classifiers: A regression approach. Pattern Recognit. 45(6): 2280-2287 (2012) - [c8]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor:
Robustness of kernel based regression: Influence and weight functions. IJCNN 2012: 1-8 - 2011
- [j8]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Kernel Regression in the Presence of Correlated Errors. J. Mach. Learn. Res. 12: 1955-1976 (2011) - [j7]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. IEEE Trans. Neural Networks 22(1): 110-120 (2011) - 2010
- [j6]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Optimized fixed-size kernel models for large data sets. Comput. Stat. Data Anal. 54(6): 1484-1504 (2010)
2000 – 2009
- 2009
- [j5]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Least conservative support and tolerance tubes. IEEE Trans. Inf. Theory 55(8): 3799-3806 (2009) - [c7]Filip Logist, Bart Huyck, Maarten Fabré, Maarten Verwerft, Bert Pluymers, Jos De Brabanter, Bart De Moor, Jan F. M. Van Impe:
Identification and control of a pilot scale binary distillation column. ECC 2009: 4659-4664 - [c6]Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens, Mia Hubert, Bart De Moor:
Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. ICANN (1) 2009: 100-110 - 2007
- [i2]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Support and Quantile Tubes. CoRR abs/cs/0703055 (2007) - 2006
- [c5]Andy Devos, Niki Bergans, Tom Dresselaers, Jos De Brabanter, Diana Maria Sima, Leentje Vanhamme, Florent Vanstapel, Paul Van Hecke, Sabine Van Huffel:
Model order selection for quantification of a multi-exponential magnetic resonance spectrum. EMBC 2006: 1299-1302 - 2005
- [j4]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
The differogram: Non-parametric noise variance estimation and its use for model selection. Neurocomputing 69(1-3): 100-122 (2005) - [j3]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Handling missing values in support vector machine classifiers. Neural Networks 18(5-6): 684-692 (2005) - [j2]Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Primal-Dual Monotone Kernel Regression. Neural Process. Lett. 22(2): 171-182 (2005) - [i1]Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Componentwise Least Squares Support Vector Machines. CoRR abs/cs/0504086 (2005) - 2003
- [c4]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Variogram based noise variance estimation and its use in kernel based regression. NNSP 2003: 199-208 - 2002
- [b1]Johan A. K. Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle:
Least Squares Support Vector Machines. World Scientific 2002, ISBN 978-981-238-151-4, pp. 1-308 - [j1]Johan A. K. Suykens, Jos De Brabanter, Lukas Lukas, Joos Vandewalle:
Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing 48(1-4): 85-105 (2002) - [c3]Lieveke Ameye, Chuan Lu, Lukas Lukas, Jos De Brabanter, Johan A. K. Suykens, Sabine Van Huffel, Hans Daniels, Gunnar Naulaers, Hugo Devlieger:
Prediction of mental development of preterm newborns at birth time using LS-SVM. ESANN 2002: 167-172 - [c2]Jos De Brabanter, Kristiaan Pelckmans, Johan A. K. Suykens, Joos Vandewalle:
Robust Cross-Validation Score Function for Non-linear Function Estimation. ICANN 2002: 713-719 - 2001
- [c1]Tony Van Gestel, Johan A. K. Suykens, Jos De Brabanter, Bart De Moor, Joos Vandewalle:
Kernel Canonical Correlation Analysis and Least Squares Support Vector Machines. ICANN 2001: 384-389
Coauthor Index
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