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Xiangrui Meng commented on SPARK-4036:
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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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