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A Note on k-support Norm Regularized Risk Minimization

Abstract : The k-support norm has been recently introduced to perform correlated sparsity regularization. Although Argyriou et al. only reported experiments using squared loss, here we apply it to several other commonly used settings resulting in novel machine learning algorithms with interesting and familiar limit cases. Source code for the algorithms described here is available.
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Preprints, Working Papers, ...
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Contributor : Matthew Blaschko Connect in order to contact the contributor
Submitted on : Wednesday, March 27, 2013 - 5:21:28 PM
Last modification on : Tuesday, March 29, 2022 - 10:58:12 AM
Long-term archiving on: : Sunday, April 2, 2017 - 9:19:39 PM


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  • HAL Id : hal-00804592, version 2
  • ARXIV : 1303.6390



Matthew Blaschko. A Note on k-support Norm Regularized Risk Minimization. 2013. ⟨hal-00804592v2⟩



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