You are viewing a plain text version of this content. The canonical link for it is here.

Posted to issues@opennlp.apache.org by "Vinh Khuc (JIRA)" <ji...@apache.org> on 2014/04/05 07:15:15 UTC

[jira] [Created] (OPENNLP-671) Add L1-regularization into L-BFGS

Vinh Khuc created OPENNLP-671:
---------------------------------

             Summary: 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
            Reporter: Vinh Khuc


L1-regularization is useful during training Maximum Entropy models since it pushes parameters of irrelevant features to zero. Hence, the trained model will be sparse and 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



--
This message was sent by Atlassian JIRA
(v6.2#6252)