Kris De Brabanter

Publication Activity (10 Years)

Years Active: 2009-2020
Publications (10 Years): 4

Top Topics

Support Vector Machine Classifiers

Ridge Regression

Top Venues

J. Mach. Learn. Res.

Pattern Recognit.

Publications

Yu Liu, Kris De Brabanter
Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients. J. Mach. Learn. Res. 21 (2020)
Yu Liu, Kris De Brabanter
Derivative Estimation in Random Design. NeurIPS (2018)
Guillermo Basulto-Elias, Alicia L. Carriquiry, Kris De Brabanter, Daniel J. Nordman
fourierin: An R package to compute Fourier integrals. R J. 9 (2) (2017)
Ahmad Alhasan, David J. White, Kris De Brabanter
Wavelet Filter Design for Pavement Roughness Analysis. Comput. Aided Civ. Infrastructure Eng. 31 (12) (2016)
Minta Thomas, Kris De Brabanter, Bart De Moor
New Bandwidth Selection Criterion for Kernel PCA: Approach to Dimensionality Reduction and Classification Problems. BMC Bioinform. 15 (2014)
Minta Thomas, Kris De Brabanter, Johan A. K. Suykens, Bart De Moor
Predicting breast cancer using an expression values weighted clinical classifier. BMC Bioinform. 15 (2014)
Kris De Brabanter, Jos De Brabanter, Bart De Moor, Irène Gijbels
Derivative estimation with local polynomial fitting. J. Mach. Learn. Res. 14 (1) (2013)
Kris De Brabanter, Bart De Moor
Deconvolution in nonparametric statistics. ESANN (2012)
Kris De Brabanter, Peter Karsmakers, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
Confidence bands for least squares support vector machine classifiers: A regression approach. Pattern Recognit. 45 (6) (2012)
Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
Robustness of kernel based regression: Influence and weight functions. IJCNN (2012)
Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens
Sparse LS-SVMs with L0 - norm minimization. ESANN (2011)
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) (2011)
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 (2011)
Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression. IEEE Trans. Neural Networks 22 (1) (2011)
Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor
Optimized fixed-size kernel models for large data sets. Comput. Stat. Data Anal. 54 (6) (2010)
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)