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https://issues.apache.org/jira/browse/FLINK-1749?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15253589#comment-15253589
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Till Rohrmann commented on FLINK-1749:
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Hi [~dedrummond],
I think Narayana is no longer working on this. Thus, you could take this issue
over. But you're right that it may make more sense to first start working on
the weak learners. For the multinomial logistic regression I haven't seen any
progress yet. So I would assume that you can take this one over, too. For the
decision trees there is a PR but the community didn't have time to review it
yet.
> Add Boosting algorithm for ensemble learning to machine learning library
> ------------------------------------------------------------------------
>
> Key: FLINK-1749
> URL: https://issues.apache.org/jira/browse/FLINK-1749
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Labels: ML
>
> Boosting [1] can help to create strong learners from an ensemble of weak
> learners and thus improving its performance. Widely used boosting algorithms
> are AdaBoost [2] and LogitBoost [3]. The work of I. Palit and C. K. Reddy [4]
> investigates how boosting can be efficiently realised in a distributed
> setting.
> Resources:
> [1] [http://en.wikipedia.org/wiki/Boosting_%28machine_learning%29]
> [2] [http://en.wikipedia.org/wiki/AdaBoost]
> [3] [http://en.wikipedia.org/wiki/LogitBoost]
> [4] [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6035709]
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