Kris De Brabanter

According to our database, Kris De Brabanter authored at least 16 papers between 2009 and 2020.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2020

Smoothed Nonparametric Derivative Estimation using Weighted Difference Quotients.

Yu Liu, Kris De Brabanter

J. Mach. Learn. Res., 2020

2018

Derivative Estimation in Random Design.

Yu Liu, Kris De Brabanter

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

fourierin: An R package to compute Fourier integrals.

Guillermo Basulto-Elias, Alicia L. Carriquiry, Kris De Brabanter, Daniel J. Nordman

R J., 2017

2016

Wavelet Filter Design for Pavement Roughness Analysis.

Ahmad Alhasan, David J. White, Kris De Brabanter

Comput. Aided Civ. Infrastructure Eng., 2016

2014

Predicting breast cancer using an expression values weighted clinical classifier.

Minta Thomas, Kris De Brabanter, Johan A. K. Suykens, Bart De Moor

BMC Bioinform., 2014

New Bandwidth Selection Criterion for Kernel PCA: Approach to Dimensionality Reduction and Classification Problems.

Minta Thomas, Kris De Brabanter, Bart De Moor

BMC Bioinform., 2014

2013

Derivative estimation with local polynomial fitting.

Kris De Brabanter, Jos De Brabanter, Bart De Moor, Irène Gijbels

J. Mach. Learn. Res., 2013

2012

Confidence bands for least squares support vector machine classifiers: A regression approach.

Kris De Brabanter, Peter Karsmakers, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor

Pattern Recognit., 2012

Robustness of kernel based regression: Influence and weight functions.

Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Deconvolution in nonparametric statistics.

Kris De Brabanter, Bart De Moor

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression.

Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor

IEEE Trans. Neural Networks, 2011

Sparse conjugate directions pursuit with application to fixed-size kernel models.

Peter Karsmakers, Kristiaan Pelckmans, Kris De Brabanter, Hugo Van hamme, Johan A. K. Suykens

Mach. Learn., 2011

Kernel Regression in the Presence of Correlated Errors.

Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor

J. Mach. Learn. Res., 2011

Sparse LS-SVMs with L0 - norm minimization.

Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

2010

Optimized fixed-size kernel models for large data sets.

Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor

Comput. Stat. Data Anal., 2010

2009

Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes.

Kris De Brabanter, Kristiaan Pelckmans, Jos De Brabanter, Michiel Debruyne, Johan A. K. Suykens, Mia Hubert, Bart De Moor

Proceedings of the Artificial Neural Networks, 2009