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From "Xiangrui Meng (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4036) Add Conditional Random Fields (CRF) algorithm to Spark MLlib
Date Tue, 24 Mar 2015 03:47:55 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4036?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14377223#comment-14377223
] 

Xiangrui Meng commented on SPARK-4036:
--------------------------------------

You don't have to use or change the Optimizer interface. It is okay to have an implementation
of gradient descent that used by CRF. We want to refactor the optimization framework, but
there is no ETA at this time. It shouldn't block this work. Before you start coding, please
prepare a design doc with the following:

1. public interfaces
2. choices of CRF algorithms and their complexities
3. limitations
...

> Add Conditional Random Fields (CRF) algorithm to Spark MLlib
> ------------------------------------------------------------
>
>                 Key: SPARK-4036
>                 URL: https://issues.apache.org/jira/browse/SPARK-4036
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Guoqiang Li
>            Assignee: Kai Sasaki
>
> Conditional random fields (CRFs) are a class of statistical modelling method often applied
in pattern recognition and machine learning, where they are used for structured prediction.

> The paper: 
> http://www.seas.upenn.edu/~strctlrn/bib/PDF/crf.pdf



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