Publications

An up-to-date version is found here.citationsdec2018

A pdf with publications is here.

Peer-reviewed Journal publications include (2005-2017):

  1. Pelckmans K., J.A.K. Suykens, B. De Moor (2005). Building Sparse Representations and Structure Determination on LS-SVM Substrates, Neurocomputing, Vol. 64, pp. 137-159.
  2. Goethals I., K. Pelckmans, J.A.K. Suykens, B. De Moor (2005). Identification of MIMO Hammerstein Models using Least Squares Support Vector Machines, Automatica, vol. 14, nr. 4, pp. 1263-1272.
  3. Pelckmans K., J. De Brabanter, J.A.K. Suykens, B. De Moor (2004). The Differogram: Nonparametric noise variance estimation and its use for model selection, Neurocomputing, vol. 69, Issues 1-3, pp. 100-122.
  4. Pelckmans K., M. Espinoza, J. De Brabanter, J.A.K. Suykens, B. De Moor (2005). Primal-Dual Monotone Kernel Regression, Neural Processing Letters, vol. 22, no. 2, Oct 2005, pp. 171-182.
  5. Pelckmans K., J. De Brabanter, J.A.K. Suykens, B. De Moor (2005). Handling Missing Values In Support Vector Machine Classifiers, Neural Networks, vol. 18, pp. 684-692.
  6. Goethals I., K. Pelckmans, J.A.K. Suykens, B. De Moor (2005). Subspace Identification of Hammerstein Systems using Least Squares Support Vector Machines, IEEE Transac- tions on Automatic Control, Special Issue on System Identification, Vol. 50, no. 10, pp. 1509-1519.
  7. Pelckmans K., J.A.K. Suykens, B. De Moor (2006). Additive regularization: fusion of training and validation levels in Kernel Methods, Machine Learning, vol. 62, no. 3, pp. 217-252.
  8. Pelckmans K., J.A.K. Suykens, B. De Moor (2007). A Convex Approach to Validation- based Learning of the Regularization Constant, IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 917-920.
  9. Van Herpe T., K. Pelckmans, J. De Brabanter, F. Janssens, B. De Moor, G. Van den Berghe (2008). Statistical approach of assessing the reliability of glucose sensors: The GLYCENSIT-procedure, Journal of Diabetes Science and Technology, vol. 2, no. 6, pp. 939-947.
  10. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S. (2010). Additive survival least squares support vector machines, Statistics in Medicine, vol. 29, no. 2, Jan, pp. 296 – 308.
  11. Babu P., Pelckmans K., Stoica P., Li J. (2010). Linear Systems, Sparse Solutions, and Sudoku, IEEE Signal Processing Letters, vol. 17, no 1.
  12. Pelckmans K., J. De Brabanter,, J.A.K. Suykens, B. De Moor (2009). Least Conservative Support and Tolerance Tubes, IEEE Transactions on Information Theory, vol. 55, no. 8, Aug. 2009, pp. 3799-3806.
  13. Suykens J.A.K., Alzate C., Pelckmans K. (2010). Primal and dual model representations in kernel-based learning, Statistics Surveys, 4, 2010, pp. 148-183.
  14. Van Belle V., Pelckmans K., Van Huffel S., Suykens J.A.K.(2010), Improved Performance on High-Dimensional Survival Data by Application of Survival-SVM. Bioinformatics. vol. 27, no. 1, Jan. 2011, pp. 87-94.
  15. Van Belle V., Pelckmans K., Suykens J.A.K., Van Huffel S. (2011) Learning Transforma- tion Models for Ranking and Survival Analysis. Journal of Machine Learning Research, vol. 12, 2011, pp. 819-862.
  16. Pelckmans K. (2010) MINLIP for the Identification of Monotone Wiener Systems. Au- tomatica, vol. 27, no. 10, 2011, pp. 2298-2305
  17. Van Belle V., Pelckmans K., Van Huffel S, Suykens, J.A.K (2011) Support vector meth- ods for survival analysis: a comparison between ranking and regression approaches. Artificial Intelligence in Medicine, vol. 53, no. 2, 2011, pp. 107-118.
  18. Karsmakers P., Pelckmans K., De Brabanter K., Van hamme H., Suykens J.A.K. (2011) Sparse conjugate directions pursuit with application to fixed-size kernel models. Machine Learning vol. 85, no. 1-2, 2011, pp. 109-148.
  19. Falck T., Dreesen P., De Brabanter K., Pelckmans K., De Moor B., Suykens J.A.K. (2012) Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems. Control Engineering Practice, Vol. 20, no. 11, 2012, pp. 1165–1174.
  20. Nygren J., Pelckmans K., and Carlsson B.. Approximate adjoint-based iterative learning control. In International Journal of Control, volume 87, number 5, pp 1028-1046, 2014.
  21. Dai L., Pelckmans K., and Bai E.-W. Identifiability and convergence analysis of the MINLIP estimator. In Automatica, volume 51, pp 104-110, 2015.
  22. Dai L. and Pelckmans K. On the nuclear norm heuristic for a Hankel matrix completion problem. In Automatica, volume 51, pp 268-272, 2015
  23. Dai L. and Pelckmans K. Sparse estimation from noisy observations of an overdetermined linear system. In Automatica, volume 50, number 11, pp 2845-2851, 2014.
  24. Xiaolin Huang, Lei Shi, Kristiaan Pelckmans, and Johan A. K. Suykens. Asymmetric nu-tube support vector regression. In Computational Statistics & Data Analysis, volume 77, pp 371-382, 2014.
  25. Dai L., Soltanalian M., and Pelckmans K. On the randomized Kaczmarz algorithm. In IEEE Signal Processing Letters, volume 21, number 3, pp 330-333, 2014.
  26. Stoica A., Pelckmans K., Rowe, W. System components of a general theory of software engineering. Science of Computer Programming, 101: 42-65 (2015).
  27. L. Yang, Pelckmans K., ’Machine Learning Approaches to Survival Analysis: Case Studies in Microarray for Breast Cancer’, in International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 483-490, 2014.
  28. Nygren J. and Pelckmans K. A direct proof of the discrete time multivariate circle and Tsypkin criteria. IEEE Transactions on Automatic Control, vol. 61, no. 2, pages 1-6, February 2016.
  29. Jensen H., Zackrisson E., Pelckmans K., Binggeli C., Ausmees K., and Lundholm U., ’A Machine-Learning Approach to Measuring Escape Of Ionizing Radiation from Galaxies in the Reionizing Epoch’ The Astrophysical Journal, Volume 827, Number 1, August 2016.
  30. Szorkovszky A.; Kotrschal A., Sumpter D. J. T. , Kolm N. and Pelckmans K. (2017) An efficient method for sorting and selecting for social behaviour. Methods in Ecology and Evolution, vol. 8 nr. 12, pp 1735–1744, DOI: 10.1111/2041-210X.12813, year 2017.
  31. Nygren J., Wigren T. and Pelckmans K. (2017) Frequency Conditions for Stable Net- worked Controllers with Time-Delay. International Journal of Control. 2017..
  32. Yasini S., Pelckmans K. (2017), Worst-case Performance Analysis of the Kalman Filter. IEEE Transactions on Automatic Control, vol. pp 1-8 no. 99, , September 2017.

 

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