| LS-SVMlab: a matlab/c toolbox for least squares support vector machines K Pelckmans, JAK Suykens, T Van Gestel, J De Brabanter, L Lukas, ... Tutorial. KULeuven-ESAT. Leuven, Belgium 142 (1-2), 2002 | 443 | 2002 |
| 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 |
| Identification of MIMO Hammerstein models using least squares support vector machines I Goethals, K Pelckmans, JAK Suykens, B De Moor Automatica 41 (7), 1263-1272, 2005 | 263 | 2005 |
| Handling missing values in support vector machine classifiers K Pelckmans, J De Brabanter, JAK Suykens, B De Moor Neural Networks 18 (5-6), 684-692, 2005 | 254 | 2005 |
| Subspace identification of Hammerstein systems using least squares support vector machines I Goethals, K Pelckmans, JAK Suykens, B De Moor IEEE Transactions on Automatic Control 50 (10), 1509-1519, 2005 | 240 | 2005 |
| Support vector methods for survival analysis: a comparison between ranking and regression approaches V Van Belle, K Pelckmans, S Van Huffel, JAK Suykens Artificial intelligence in medicine 53 (2), 107-118, 2011 | 221 | 2011 |
| Convex clustering shrinkage K Pelckmans, J De Brabanter, JAK Suykens, B De Moor PASCAL workshop on statistics and optimization of clustering workshop 1524, 2005 | 148 | 2005 |
| Support vector machines for survival analysis V Van Belle, K Pelckmans, JAK Suykens, S Van Huffel Proceedings of the third international conference on computational …, 2007 | 121 | 2007 |
| Improved performance on high-dimensional survival data by application of Survival-SVM V Van Belle, K Pelckmans, S Van Huffel, JAK Suykens Bioinformatics 27 (1), 87-94, 2011 | 87 | 2011 |
| 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 |
| LS-SVMlab toolbox user’s guide K Pelckmans, JAK Suykens, T Van Gestel, J De Brabanter, L Lukas, ... Pattern recognition letters 24 (2003), 659-675, 2003 | 83 | 2003 |
| Multi-class kernel logistic regression: a fixed-size implementation P Karsmakers, K Pelckmans, JAK Suykens 2007 International Joint Conference on Neural Networks, 1756-1761, 2007 | 75 | 2007 |
| 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 | 70 | 2012 |
| LS-SVMlab toolbox user’s guide version 1.5 K Pelckmans, JAK Suykens, T Van Gestel, J De Brabanter, L Lukas, ... Katholiede Univeristeit Leuven, Belgium, unpublished. Available http://www …, 2003 | 61 | 2003 |
| Building sparse representations and structure determination on LS-SVM substrates K Pelckmans, JAK Suykens, B De Moor Neurocomputing 64, 137-159, 2005 | 57 | 2005 |
| Identification of wiener-hammerstein systems using LS-SVMs T Falck, K Pelckmans, JAK Suykens, B De Moor IFAC Proceedings Volumes 42 (10), 820-825, 2009 | 54 | 2009 |
| A comparative study of LS-SVM’s applied to the silver box identification problem M Espinoza, K Pelckmans, L Hoegaerts, JAK Suykens, B De Moor IFAC Proceedings Volumes 37 (13), 369-374, 2004 | 53 | 2004 |
| Additive survival least‐squares support vector machines V Van Belle, K Pelckmans, JAK Suykens, S Van Huffel Statistics in Medicine 29 (2), 296-308, 2010 | 52 | 2010 |
| Learning Transformation Models for Ranking and Survival Analysis. V Van Belle, K Pelckmans, JAK Suykens, S Van Huffel Journal of machine learning research 12 (3), 2011 | 51 | 2011 |
| Survival SVM: a practical scalable algorithm. V Van Belle, K Pelckmans, JAK Suykens, S Van Huffel ESANN, 89-94, 2008 | 47 | 2008 |
