GitHub - gkirtzou/ksup_svm: k-support regularized Support Vector Machine (ksup-SVM) is a novel regularization method that extends the L1 regularized SVM to a mixed norm of both L1 and L2 norms. This enables the use of a correlated sparsity regularization with the power of the SVM framework.

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k-support regularized Support Vector Machine (ksup-SVM) is a novel regularization method that extends the L1 regularized SVM to a mixed norm of both L1 and L2 norms. This enables the use of a correlated sparsity regularization with the power of the SVM framework.

GPL-3.0 license

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Name		Name	Last commit message	Last commit date
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sparse_k		sparse_k
COPYING.txt		COPYING.txt
README.txt		README.txt
RunExampleKSuppSVM.m		RunExampleKSuppSVM.m
center.m		center.m
ksupSVM.m		ksupSVM.m
normalize.m		normalize.m

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%%%%%%%%%%%%%%%%%%%%%%%%% LINCENSE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ksup-SVM package 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.

ksup-SVM package is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with KsupportNormFMRICode. If not, see <http://www.gnu.org/licenses/>.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DISCLAIMER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ksup-SVM package contains an implementation of k-support regularized SVM, 
which is code for the Huber approximation to the Hinge loss and code of
ksupport norm penalty (folder sparse_k), which is also free software
under GNU GPL licence and it is provided in the webpage  :: 
http://www.centrale-ponts.fr/personnel/andreas/code/sparse_k/sparse_k.tar

It was tested in matlab version 2008b and provides no warranties. 

When using the provided ksup-SVM package for scientific work
please cite the following two papers

1) Sparse classification with MRI based markers for neuromuscular disease
   categoriztion.
   Gkirtzou Katerina, Deux Jean-François, Bassez Guillaume, Sotiras Aristeidis,
   Rahmouni Alain, Varacca Thibault, Paragios Nikos and Blaschko B. Matthew
   4th International Workshop on Machine Learning in Medical Imaging (MLMI), 2013.

2) Sparse Prediction with the k-Support Norm 
   Andreas Argyriou, Rina Foygel and Nathan Srebro
   Neural Information Processing Systems (NIPS), pp. 1466-1474, 2012