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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2368) Add a convergence criteria
Date Thu, 16 Jul 2015 13:54:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2368?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14629757#comment-14629757
] 

ASF GitHub Bot commented on FLINK-2368:
---------------------------------------

GitHub user sachingoel0101 opened a pull request:

    https://github.com/apache/flink/pull/918

    [FLINK-2368][ml]Adds convergence criteria [WIP]

    Adds a convergence criteria class which allows the user to decide whether they want to
terminate training at any point, based on the solutions before and after an iteration. 
    
    Adding the first example to the SVM trainer.[Not finished yet].

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/sachingoel0101/flink convergence

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/flink/pull/918.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #918
    
----
commit 656010cd1ef7df5bffafadb6a8be9bd7db715533
Author: Sachin Goel <sachingoel0101@gmail.com>
Date:   2015-07-16T13:50:57Z

    Initial framework design

----


> Add a convergence criteria
> --------------------------
>
>                 Key: FLINK-2368
>                 URL: https://issues.apache.org/jira/browse/FLINK-2368
>             Project: Flink
>          Issue Type: Bug
>          Components: Machine Learning Library
>            Reporter: Sachin Goel
>            Assignee: Sachin Goel
>
> Add a convergence criteria for the Machine Learning library. If an algorithm is iteration
based, it should have a setConvergenceCritieria function which will check for convergence
at the end of every iteration based on the user defined criteria. 
> Algorithm writer will provide the user will the training data set, solution before an
iteration and solution after the iteration and user will have to return an empty data set
in case the training needs to be terminated.



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