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ORCIDKristiaan Pelckmans
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2020 – today
- 2024
[i12]Abel Souza, Kristiaan Pelckmans, Johan Tordsson:
A HPC Co-Scheduler with Reinforcement Learning. CoRR abs/2401.09706 (2024)- [i11]Abel Souza, Kristiaan Pelckmans, Devarshi Ghoshal, Lavanya Ramakrishnan, Johan Tordsson:
ASA - The Adaptive Scheduling Algorithm. CoRR abs/2401.09733 (2024) - 2021
- [c32]Abel Souza
, Kristiaan Pelckmans, Johan Tordsson:
A HPC Co-scheduler with Reinforcement Learning. JSSPP 2021: 126-148 - [i10]Kristiaan Pelckmans, Moustafa Aboushady, Andreas Brosemyr:
Detecting Suspicious Events in Fast Information Flows. CoRR abs/2101.02424 (2021) - 2020
- [j27]Kristiaan Pelckmans:
Monitoring High-Frequency Data Streams in FinTech: FADO Versus $K$K-Means. IEEE Intell. Syst. 35(2): 36-42 (2020) - [c31]Abel Souza, Kristiaan Pelckmans, Devarshi Ghoshal, Lavanya Ramakrishnan, Johan Tordsson:
ASA - The Adaptive Scheduling Architecture. HPDC 2020: 161-165 - [i9]Kristiaan Pelckmans, Liu Yang:
APTER: Aggregated Prognosis Through Exponential Reweighting. CoRR abs/2002.08731 (2020) - [i8]Kristiaan Pelckmans, Hong-Li Zeng:
Longitudinal Support Vector Machines for High Dimensional Time Series. CoRR abs/2002.09763 (2020)
2010 – 2019
- 2019
- [j26]Johannes Nygren, Torbjörn Wigren, Kristiaan Pelckmans:
Frequency conditions for stable networked controllers with time-delay. Int. J. Control 92(4): 886-895 (2019) - [c30]Yang Liu, Kristiaan Pelckmans:
APTER: Aggregated Prognosis Through Exponential Re-weighting. COCOON 2019: 425-436 - 2018
- [j25]Johannes Nygren, Kristiaan Pelckmans:
A stability criterion for switching Lur'e systems with switching-path restrictions. Autom. 96: 337-341 (2018) - [j24]Sholeh Yasini, Kristiaan Pelckmans:
Worst-Case Prediction Performance Analysis of the Kalman Filter. IEEE Trans. Autom. Control. 63(6): 1768-1775 (2018) - 2017
- [i7]Kristiaan Pelckmans:
FADO: A Deterministic Detection/Learning Algorithm. CoRR abs/1711.02361 (2017) - 2016
- [j23]Johannes Nygren, Kristiaan Pelckmans:
A Direct Proof of the Discrete-Time Multivariate Circle and Tsypkin Criteria. IEEE Trans. Autom. Control. 61(2): 544-549 (2016) - 2015
- [j22]Liang Dai, Kristiaan Pelckmans, Er-Wei Bai:
Identifiability and convergence analysis of the MINLIP estimator. Autom. 51: 104-110 (2015) - [j21]Liang Dai, Kristiaan Pelckmans:
On the nuclear norm heuristic for a Hankel matrix completion problem. Autom. 51: 268-272 (2015) - [j20]Anca-Juliana Stoica, Kristiaan Pelckmans, William Rowe:
System components of a general theory of software engineering. Sci. Comput. Program. 101: 42-65 (2015) - [c29]Johannes Nygren, Kristiaan Pelckmans:
Stability analysis of an adaptively sampled controller for SISO systems with nonlinear feedback. ACC 2015: 5353-5358 - [c28]Johannes Nygren, Kristiaan Pelckmans:
Conditions for input-output stability of discrete-time Lur'e systems with time-varying delays. CDC 2015: 7707-7714 - 2014
- [j19]Liang Dai, Kristiaan Pelckmans:
Sparse estimation from noisy observations of an overdetermined linear system. Autom. 50(11): 2845-2851 (2014) - [j18]Xiaolin Huang, Lei Shi, Kristiaan Pelckmans, Johan A. K. Suykens:
Asymmetric v-tube support vector regression. Comput. Stat. Data Anal. 77: 371-382 (2014) - [j17]Johannes Nygren, Kristiaan Pelckmans, Bengt Carlsson:
Approximate adjoint-based iterative learning control. Int. J. Control 87(5): 1028-1046 (2014) - [j16]Liang Dai, Mojtaba Soltanalian, Kristiaan Pelckmans:
On the Randomized Kaczmarz Algorithm. IEEE Signal Process. Lett. 21(3): 330-333 (2014) - [i6]Liang Dai, Mojtaba Soltanalian, Kristiaan Pelckmans:
On the Randomized Kaczmarz Algorithm. CoRR abs/1402.2863 (2014) - [i5]Liang Dai, Kristiaan Pelckmans:
Sparse Estimation From Noisy Observations of an Overdetermined Linear System. CoRR abs/1402.2864 (2014) - 2013
- [c27]Kristiaan Pelckmans:
Randomized Gossip Algorithms for Achieving Consensus on the Majority Vote. ALCOSP 2013: 275-280 - 2012
- [c26]Liang Dai, Kristiaan Pelckmans:
An ellipsoid based, two-stage screening test for BPDN. EUSIPCO 2012: 654-658 - [i4]Liang Dai, Kristiaan Pelckmans:
On the Nuclear Norm heuristic for a Hankel matrix Recovery Problem. CoRR abs/1207.4420 (2012) - 2011
- [j15]Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens:
Support vector methods for survival analysis: a comparison between ranking and regression approaches. Artif. Intell. Medicine 53(2): 107-118 (2011) - [j14]Kristiaan Pelckmans:
MINLIP for the identification of monotone Wiener systems. Autom. 47(10): 2298-2305 (2011) - [j13]Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens:
Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinform. 27(1): 87-94 (2011) - [j12]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Learning Transformation Models for Ranking and Survival Analysis. J. Mach. Learn. Res. 12: 819-862 (2011) - [j11]Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens:
Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach. Learn. 85(1-2): 109-148 (2011) - [c25]Kristiaan Pelckmans:
An Adaptive Compression Algorithm in a Deterministic World. Algorithmic Probability and Friends 2011: 299-305 - [c24]Kristiaan Pelckmans, Liang Dai:
A simple recursive algorithm for learning a Monotone Wiener system. CDC/ECC 2011: 3622-3627 - 2010
- [j10]Prabhu Babu, Kristiaan Pelckmans, Petre Stoica, Jian Li:
Linear Systems, Sparse Solutions, and Sudoku. IEEE Signal Process. Lett. 17(1): 40-42 (2010) - [c23]Kristiaan Pelckmans:
On the identification of monotone Wiener systems. CDC 2010: 7208-7213 - [c22]Marco Signoretto, Kristiaan Pelckmans, Lieven De Lathauwer, Johan A. K. Suykens:
Improved non-parametric sparse recovery with data matched penalties. CIP 2010: 46-51 - [c21]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
On the use of a clinical kernel in survival analysis. ESANN 2010 - [c20]Kristiaan Pelckmans, Toon van Waterschoot, Johan A. K. Suykens:
Efficient adaptive filtering for smooth linear FIR models. EUSIPCO 2010: 2136-2140 - [i3]Kristiaan Pelckmans:
MINLIP for the Identification of Monotone Wiener Systems. CoRR abs/1006.4832 (2010)
2000 – 2009
- 2009
- [j9]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Least conservative support and tolerance tubes. IEEE Trans. Inf. Theory 55(8): 3799-3806 (2009) - [c19]Kristiaan Pelckmans, Johan A. K. Suykens:
Transductively Learning from Positive Examples Only. ESANN 2009 - [c18]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
MINLIP: Efficient Learning of Transformation Models. ICANN (1) 2009: 60-69 - [c17]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 - [c16]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
- [c15]Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Survival SVM: a practical scalable algorithm. ESANN 2008: 89-94 - [c14]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
- [j8]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Convex Approach to Validation-Based Learning of the Regularization Constant. IEEE Trans. Neural Networks 18(3): 917-920 (2007) - [c13]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Convex optimization for the design of learning machines. ESANN 2007: 193-204 - [c12]Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens:
Multi-class kernel logistic regression: a fixed-size implementation. IJCNN 2007: 1756-1761 - [c11]Peter Karsmakers, Kristiaan Pelckmans, Johan A. K. Suykens, Hugo Van hamme:
Fixed-size kernel logistic regression for phoneme classification. INTERSPEECH 2007: 78-81 - [c10]Kristiaan Pelckmans, Johan A. K. Suykens:
Transductive Rademacher Complexities for Learning Over a Graph. MLG 2007 - [c9]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
A Risk Minimization Principle for a Class of Parzen Estimators. NIPS 2007: 1137-1144 - [c8]Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor:
Margin based Transductive Graph Cuts using Linear Programming. AISTATS 2007: 363-370 - [i2]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Support and Quantile Tubes. CoRR abs/cs/0703055 (2007) - 2006
- [j7]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Additive Regularization Trade-Off: Fusion of Training and Validation Levels in Kernel Methods. Mach. Learn. 62(3): 217-252 (2006) - 2005
- [j6]Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Identification of MIMO Hammerstein models using least squares support vector machines. Autom. 41(7): 1263-1272 (2005) - [j5]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Building sparse representations and structure determination on LS-SVM substrates. Neurocomputing 64: 137-159 (2005) - [j4]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) - [j3]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) - [j2]Kristiaan Pelckmans, Marcelo Espinoza, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Primal-Dual Monotone Kernel Regression. Neural Process. Lett. 22(2): 171-182 (2005) - [j1]Ivan Goethals, Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Subspace identification of Hammerstein systems using least squares support vector machines. IEEE Trans. Autom. Control. 50(10): 1509-1519 (2005) - [c7]Kristiaan Pelckmans, Johan A. K. Suykens, Ivan Goethals, Bart De Moor:
On Model Complexity Control in Identification of Hammerstein Systems. CDC/ECC 2005: 1203-1208 - [c6]Ivan Goethals, Kristiaan Pelckmans, Luc Hoegaerts, Johan A. K. Suykens, Bart De Moor:
Subspace intersection identification of Hammerstein-Wiener systems. CDC/ECC 2005: 7108-7113 - [c5]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Componentwise Support Vector Machines for Structure Detection. ICANN (2) 2005: 643-648 - [i1]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) - 2004
- [c4]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Sparse LS-SVMs using additive regularization with a penalized validation criterion. ESANN 2004: 435-440 - [c3]Kristiaan Pelckmans, Johan A. K. Suykens, Bart De Moor:
Morozov, Ivanov and Tikhonov Regularization Based LS-SVMs. ICONIP 2004: 1216-1222 - 2003
- [c2]Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Variogram based noise variance estimation and its use in kernel based regression. NNSP 2003: 199-208 - 2002
- [c1]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
Coauthor Index
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