 | 2009 |
| 22 |  | 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 |
| 21 |  | Vanya Van Belle,
Kristiaan Pelckmans,
Johan A. K. Suykens,
Sabine Van Huffel:
MINLIP: Efficient Learning of Transformation Models.
ICANN (1) 2009: 60-69 |
| 20 |  | Vanya Van Belle,
Kristiaan Pelckmans,
Johan A. K. Suykens,
Sabine Van Huffel:
Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints.
IWANN (1) 2009: 65-72 |
| 2008 |
| 19 |  | Vanya Van Belle,
Kristiaan Pelckmans,
Johan A. K. Suykens,
Sabine Van Huffel:
Survival SVM: a practical scalable algorithm.
ESANN 2008: 89-94 |
| 18 |  | Marco Signoretto,
Kristiaan Pelckmans,
Johan A. K. Suykens:
Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation.
ICANN (1) 2008: 175-184 |
| 2007 |
| 17 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Convex optimization for the design of learning machines.
ESANN 2007: 193-204 |
| 16 |  | Peter Karsmakers,
Kristiaan Pelckmans,
Johan A. K. Suykens:
Multi-class kernel logistic regression: a fixed-size implementation.
IJCNN 2007: 1756-1761 |
| 15 |  | Kristiaan Pelckmans,
Johan A. K. Suykens:
Transductive Rademacher Complexities for Learning Over a Graph.
MLG 2007 |
| 14 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
A Risk Minimization Principle for a Class of Parzen Estimators.
NIPS 2007 |
| 13 |  | Kristiaan Pelckmans,
Jos De Brabanter,
Johan A. K. Suykens,
Bart De Moor:
Support and Quantile Tubes
CoRR abs/cs/0703055: (2007) |
| 12 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
A Convex Approach to Validation-Based Learning of the Regularization Constant.
IEEE Transactions on Neural Networks 18(3): 917-920 (2007) |
| 2006 |
| 11 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods.
Machine Learning 62(3): 217-252 (2006) |
| 2005 |
| 10 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Componentwise Support Vector Machines for Structure Detection.
ICANN (2) 2005: 643-648 |
| 9 |  | Ivan Goethals,
Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Identification of MIMO Hammerstein models using least squares support vector machines.
Automatica 41(7): 1263-1272 (2005) |
| 8 |  | 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) |
| 7 |  | 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) |
| 6 |  | Kristiaan Pelckmans,
Marcelo Espinoza,
Jos De Brabanter,
Johan A. K. Suykens,
Bart De Moor:
Primal-Dual Monotone Kernel Regression.
Neural Processing Letters 22(2): 171-182 (2005) |
| 5 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Building sparse representations and structure determination on LS-SVM substrates.
Neurocomputing 64: 137-159 (2005) |
| 4 |  | 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) |
| 2004 |
| 3 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Sparse LS-SVMs using additive regularization with a penalized validation criterion.
ESANN 2004: 435-440 |
| 2 |  | Kristiaan Pelckmans,
Johan A. K. Suykens,
Bart De Moor:
Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs.
ICONIP 2004: 1216-1222 |
| 2002 |
| 1 |  | 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 |