| 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 | 366 | 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 | 165 | 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 | 143 | 2010 |
| Derivative Estimation with Local Polynomial Fitting. K De Brabanter, J De Brabanter, B De Moor, I Gijbels Journal of Machine Learning Research 14 (1), 2013 | 105 | 2013 |
| Robustness of kernel based regression: a comparison of iterative weighting schemes K De Brabanter, K Pelckmans, J De Brabanter, M Debruyne, JAK Suykens, ... Artificial Neural Networks–ICANN 2009: 19th International Conference …, 2009 | 84 | 2009 |
| 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 | 82 | 2011 |
| 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 | 69 | 2012 |
| 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 | 42 | 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 | 39 | 2017 |
| 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 | 39 | 2009 |
| 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 | 36 | 2012 |
| 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 | 33 | 2019 |
| Nonparametric regression via StatLSSVM K De Brabanter, J Suykens, B De Moor | 33 | 2013 |
| Automatic classification of bloodstain patterns caused by gunshot and blunt impact at various distances Y Liu, D Attinger, K De Brabanter Journal of forensic sciences 65 (3), 729-743, 2020 | 31 | 2020 |
| New bandwidth selection criterion for Kernel PCA: Approach to dimensionality reduction and classification problems M Thomas, KD Brabanter, BD Moor BMC bioinformatics 15, 1-12, 2014 | 30 | 2014 |
| 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 | 27 | 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 | 26 | 2016 |
| 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 | 25 | 2018 |
| Local polynomial regression with correlated errors in random design and unknown correlation structure K De Brabanter, F Cao, I Gijbels, J Opsomer Biometrika 105 (3), 681-690, 2018 | 24 | 2018 |
| Predicting breast cancer using an expression values weighted clinical classifier M Thomas, KD Brabanter, JAK Suykens, BD Moor BMC bioinformatics 15, 1-11, 2014 | 24 | 2014 |
