Kris De Brabanter
Kris De Brabanter
Associate Professor of Statistics and Industrial Manufacturing & Systems Engineering
Verified email at - Homepage
Cited by
Cited by
LS-SVMlab toolbox user's guide: version 1.7
K De Brabanter, P Karsmakers, F Ojeda, C Alzate, J De Brabanter, ...
Katholieke Universiteit Leuven, 2010
Approximate confidence and prediction intervals for least squares support vector regression
K De Brabanter, J De Brabanter, JAK Suykens, B De Moor
IEEE Transactions on Neural Networks 22 (1), 110-120, 2010
Optimized fixed-size kernel models for large data sets
K De Brabanter, J De Brabanter, JAK Suykens, B De Moor
Computational Statistics & Data Analysis 54 (6), 1484-1504, 2010
Robustness of kernel based regression: a comparison of iterative weighting schemes
KD Brabanter, K Pelckmans, JD Brabanter, M Debruyne, JAK Suykens, ...
International conference on artificial neural networks, 100-110, 2009
Derivative estimation with local polynomial fitting
K De Brabanter, J De Brabanter, I Gijbels, B De Moor
Journal of Machine Learning Research 14 (1), 281-301, 2013
Least-squares support vector machines for the identification of Wiener–Hammerstein systems
T Falck, P Dreesen, K De Brabanter, K Pelckmans, B De Moor, ...
Control Engineering Practice 20 (11), 1165-1174, 2012
Kernel Regression in the Presence of Correlated Errors.
K De Brabanter, J De Brabanter, JAK Suykens, B De Moor
Journal of Machine Learning Research 12 (6), 2011
Least squares support vector regression with applications to large-scale data: a statistical approach
K De Brabanter
Faculty of Engineering, KU Leuven, Katholieke Universiteit Leuven, 2011
Fixed-size LS-SVM applied to the Wiener-Hammerstein benchmark
K De Brabanter, P Dreesen, P Karsmakers, K Pelckmans, J De Brabanter, ...
IFAC Proceedings Volumes 42 (10), 826-831, 2009
Nonparametric regression via StatLSSVM
K De Brabanter, J Suykens, B De Moor
Journal of Statistical Software 55 (2), 1-22, 2013
Confidence bands for least squares support vector machine classifiers: A regression approach
K De Brabanter, P Karsmakers, J De Brabanter, JAK Suykens, B De Moor
Pattern Recognition 45 (6), 2280-2287, 2012
Sparse LSSVMs with L0-norm minimization
J Lopez, K De Brabanter, JR Dorronsoro, JAK Suykens
Proceedings of the European symposium on artificial neural networks …, 2011
Spatial pavement roughness from stationary laser scanning
A Alhasan, DJ White, K De Brabanter
International Journal of Pavement Engineering 18 (1), 83-96, 2017
Sparse conjugate directions pursuit with application to fixed-size kernel models
P Karsmakers, K Pelckmans, K De Brabanter, JAK Suykens
Machine learning 85 (1), 109-148, 2011
Wavelet filter design for pavement roughness analysis
A Alhasan, DJ White, K De Brabanter
Computer‐Aided Civil and Infrastructure Engineering 31 (12), 907-920, 2016
Determining the region of origin of blood spatter patterns considering fluid dynamics and statistical uncertainties
D Attinger, PM Comiskey, AL Yarin, K De Brabanter
Forensic science international 298, 323-331, 2019
Predicting breast cancer using an expression values weighted clinical classifier
M Thomas, K De Brabanter, JAK Suykens, B De Moor
BMC bioinformatics 15 (1), 1-11, 2014
New bandwidth selection criterion for Kernel PCA: approach to dimensionality reduction and classification problems
M Thomas, KD Brabanter, BD Moor
BMC bioinformatics 15 (1), 1-12, 2014
Identification of a pilot scale distillation column: a kernel based approach
B Huyck, K De Brabanter, F Logist, J De Brabanter, J Van Impe, ...
IFAC Proceedings Volumes 44 (1), 471-476, 2011
A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Impact beating spatters
D Attinger, Y Liu, T Bybee, K De Brabanter
Data in brief 18, 648-654, 2018
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