Code for the machine learning methods described in: M. B. Blaschko, A Note on k-support Norm Regularized Risk Minimization. arXiv:1303.6390, 2013.
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README.md
experiments.m
ksupEpsilonInsensitive.m
ksupExponentialLoss.m
ksupLeastAbsDeviations.m
ksupLeastSquares.m
ksupLogisticRegression.m
ksupOneSideLeastSquares.m
ksupSVM.m
norm_overlap.m
overlap_nest.m
prox_overlap.m

README.md

ksupport

Code for the machine learning methods described in: M. B. Blaschko, A Note on k-support Norm Regularized Risk Minimization. arXiv:1303.6390, 2013. http://hal.inria.fr/hal-00804592

Papers that have used this code include: Sidahmed, H., E. Prokofyeva, and M. B. Blaschko: Discovering Predictors of Mental Health Service Utilization with k-support Regularized Logistic Regression. Information Sciences, 2015.

Belilovsky, E., K. Gkirtzou, M. Misyrlis, A. B. Konova, J. Honorio, N. Alia-Klein, R. Z. Goldstein, D. Samaras, and M. B. Blaschko: Predictive sparse modeling of fMRI data for improved classication, regression, and visualization using the k-support norm. Computerized Medical Imaging and Graphics, 2015.

Author: Matthew Blaschko - matthew.blaschko@inria.fr Copyright (c) 2013

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

Start with experiments.m - runs each type of loss with a k-support norm regularizer

If you use this software in your research, please cite:

M. B. Blaschko, A Note on k-support Norm Regularized Risk Minimization. arXiv:1303.6390, 2013.

Argyriou, A., Foygel, R., Srebro, N.: Sparse prediction with the k-support norm. NIPS. pp. 1466-1474 (2012)