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Kristiaan Pelckmans: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Sparse LS-SVMs using additive regularization with a penalized validation criterion. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:435-440 [Conf]
  2. Jos De Brabanter, Kristiaan Pelckmans, Johan A. K. Suykens, Joos Vandewalle
    Robust Cross-Validation Score Function for Non-linear Function Estimation. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:713-719 [Conf]
  3. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Componentwise Support Vector Machines for Structure Detection. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:643-648 [Conf]
  4. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:1216-1222 [Conf]
  5. 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. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:69, n:1-3, pp:100-122 [Journal]
  6. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Building sparse representations and structure determination on LS-SVM substrates. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:137-159 [Journal]
  7. Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor
    Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2006, v:62, n:3, pp:217-252 [Journal]
  8. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Handling missing values in support vector machine classifiers. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:684-692 [Journal]
  9. Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Primal-Dual Monotone Kernel Regression. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2005, v:22, n:2, pp:171-182 [Journal]
  10. Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Support and Quantile Tubes [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  11. Kristiaan Pelckmans, Ivan Goethals, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
    Componentwise Least Squares Support Vector Machines [Citation Graph (0, 0)][DBLP]
    CoRR, 2005, v:0, n:, pp:- [Journal]

  12. Convex optimization for the design of learning machines. [Citation Graph (, )][DBLP]


  13. Survival SVM: a practical scalable algorithm. [Citation Graph (, )][DBLP]


  14. Quadratically Constrained Quadratic Programming for Subspace Selection in Kernel Regression Estimation. [Citation Graph (, )][DBLP]


  15. MINLIP: Efficient Learning of Transformation Models. [Citation Graph (, )][DBLP]


  16. Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. [Citation Graph (, )][DBLP]


  17. Multi-class kernel logistic regression: a fixed-size implementation. [Citation Graph (, )][DBLP]


  18. Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints. [Citation Graph (, )][DBLP]


  19. A Risk Minimization Principle for a Class of Parzen Estimators. [Citation Graph (, )][DBLP]


  20. Transductive Rademacher Complexities for Learning Over a Graph. [Citation Graph (, )][DBLP]


  21. MINLIP for the Identification of Monotone Wiener Systems [Citation Graph (, )][DBLP]


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