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https://issues.apache.org/jira/browse/FLINK-1749?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15253007#comment-15253007
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David E Drummond commented on FLINK-1749:
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Hi [~trohrm...@apache.org],
Are there any updates on this issue? I am a full-time data engineer and I
would enjoy contributing to this. In particular, I would like to start by
working on the LogitBoost that you referred to in reference [3], with the
distributed approach discussed in [4].
> 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
> Assignee: narayana reddy
> 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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