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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2047) Rename CoCoA to SVM
Date Wed, 27 May 2015 10:02:17 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2047?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14560719#comment-14560719
] 

ASF GitHub Bot commented on FLINK-2047:
---------------------------------------

GitHub user thvasilo opened a pull request:

    https://github.com/apache/flink/pull/733

    [FLINK-2047] [ml]  Rename CoCoA to SVM

    The CoCoA algorithm as implemented functions as an SVM classifier.
    
    As CoCoA mostly concerns the optimization process and not the actual learning algorithm,
it makes sense to rename the learner to SVM which users are more familiar with.

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

    $ git pull https://github.com/thvasilo/flink cocoa-svm

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

    https://github.com/apache/flink/pull/733.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 #733
    
----
commit 5768235ca73de8b03e9a523b7853a57f058806f1
Author: Theodore Vasiloudis <tvas@sics.se>
Date:   2015-05-27T10:00:17Z

    Renaming CoCoA to SVM

----


> Rename CoCoA to SVM
> -------------------
>
>                 Key: FLINK-2047
>                 URL: https://issues.apache.org/jira/browse/FLINK-2047
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Theodore Vasiloudis
>            Assignee: Theodore Vasiloudis
>            Priority: Trivial
>             Fix For: 0.9
>
>
> The CoCoA algorithm as implemented functions as an SVM classifier.
> As CoCoA mostly concerns the optimization process and not the actual learning algorithm,
it makes sense to rename the learner to SVM which users are more familiar with.
> In the future we would like to use the CoCoA algorithm to solve more large scale optimization
problems for other learning algorithms.



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