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Kris De Brabanter
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2020 – today
- 2020
- [j11]Yu Liu, Kris De Brabanter:
Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients. J. Mach. Learn. Res. 21: 65:1-65:45 (2020)
2010 – 2019
- 2018
- [c5]Yu Liu, Kris De Brabanter:
Derivative Estimation in Random Design. NeurIPS 2018: 3449-3458 - 2017
- [j10]Guillermo Basulto-Elias
, Alicia L. Carriquiry, Kris De Brabanter, Daniel J. Nordman:
fourierin: An R package to compute Fourier integrals. R J. 9(2): 72 (2017) - 2016
- [j9]Ahmad Alhasan, David J. White, Kris De Brabanter:
Wavelet Filter Design for Pavement Roughness Analysis. Comput. Aided Civ. Infrastructure Eng. 31(12): 907-920 (2016) - 2014
- [j8]Minta Thomas, Kris De Brabanter
New Bandwidth Selection Criterion for Kernel PCA: Approach to Dimensionality Reduction and Classification Problems. BMC Bioinform. 15: 137 (2014) - [j7]Minta Thomas, Kris De Brabanter
Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinform. 15: 6603 (2014) - 2013
- [j6]Kris De Brabanter, Jos De Brabanter, Bart De Moor, Irène Gijbels:
Derivative estimation with local polynomial fitting. J. Mach. Learn. Res. 14(1): 281-301 (2013) - 2012
- [j5]Kris De Brabanter, Peter Karsmakers
Confidence bands for least squares support vector machine classifiers: A regression approach. Pattern Recognit. 45(6): 2280-2287 (2012) - [c4]Kris De Brabanter, Bart De Moor:
Deconvolution in nonparametric statistics. ESANN 2012 - [c3]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens
Robustness of kernel based regression: Influence and weight functions. IJCNN 2012: 1-8 - 2011
- [j4]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
Kernel Regression in the Presence of Correlated Errors. J. Mach. Learn. Res. 12: 1955-1976 (2011) - [j3]Peter Karsmakers
Sparse conjugate directions pursuit with application to fixed-size kernel models. Mach. Learn. 85(1-2): 109-148 (2011) - [j2]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. IEEE Trans. Neural Networks 22(1): 110-120 (2011) - [c2]Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens:
Sparse LS-SVMs with L0 - norm minimization. ESANN 2011 - 2010
- [j1]Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens
Optimized fixed-size kernel models for large data sets. Comput. Stat. Data Anal. 54(6): 1484-1504 (2010)
2000 – 2009
- 2009
- [c1]Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens
Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes. ICANN (1) 2009: 100-110
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