[jira] [Commented] (SPARK-2273) online learning algorithms: Passive Aggressive

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-2273?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14621404#comment-14621404 ] 

Joseph K. Bradley commented on SPARK-2273:
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I just saw this old JIRA.  It would be interesting to see some online algorithms implemented for Spark Streaming.  It would be good to get a sense of which ones are highest-priority and have real-world use cases.  If you or others are still interested in this, I think it will be important to survey use cases and key algorithms before beginning implementation.

> online learning algorithms: Passive Aggressive
> ----------------------------------------------
>
>                 Key: SPARK-2273
>                 URL: https://issues.apache.org/jira/browse/SPARK-2273
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Jun Xie
>            Priority: Minor
>              Labels: online_learning
>
> Margin-based online learning algorithms can be used in various prediction tasks, including binary and multiclass categorization, regression, uniclass prediction and sequence prediction. The update steps of different algorithms can be unified to a simple constraint optimization problem. 
> Koby Crammer (2006) proposed online passive-aggressive algorithm to optimize the proposed constraint problem. It can be used in large-scale problem and maintain a high accuracy of the classifiers by a proved smallest loss which can be attained by any fixed hypothesis.
> Reference:
> Online Passive-Aggressive Algorithms. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer. JMLR. 7(Mar):551--585, 2006.



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