[
https://issues.apache.org/jira/browse/FLINK-1749?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15253310#comment-15253310
]
David E Drummond commented on FLINK-1749:
-----------------------------------------
I noticed that multinomial logistic regression [FLINK-1743] is still open and
unresolved, and would be a dependency for LogitBoost as a weak learner.
Similarly, AdaBoost may require decision trees [FLINK-1727] as a weak
classifier. It may make more sense to work on those projects prior to boosting?
> 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]
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
