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https://issues.apache.org/jira/browse/OPENNLP-671?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Vinh Khuc closed OPENNLP-671.
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Resolution: Fixed
> Add L1-regularization into L-BFGS
> ---------------------------------
>
> Key: OPENNLP-671
> URL: https://issues.apache.org/jira/browse/OPENNLP-671
> Project: OpenNLP
> Issue Type: Improvement
> Components: Machine Learning
> Affects Versions: tools-1.5.3, maxent-3.0.3
> Reporter: Vinh Khuc
> Fix For: 1.6.0
>
> Attachments: L1-ElasticNet-LBFGS.patch, nl-per-testa-l1.log,
> nl-per-testb-l1.log, nl-per-train-l1.log, qn-trainer-l1.params
>
>
> L1-regularization is useful during training Maximum Entropy models since it
> pushes parameters of irrelevant features to zero. Hence, the parameter vector
> will be sparse and the trained model will be compact.
> When the number of features is much larger than the number of training
> examples, L1 often gives better accuracy than L2.
> The implementation of L1-regularization for L-BFGS will follow the method
> described in the paper:
> http://research.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf
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