[
https://issues.apache.org/jira/browse/SPARK-4638?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15840711#comment-15840711
]
Joseph K. Bradley commented on SPARK-4638:
------------------------------------------
Commenting here b/c of the recent dev list thread: Non-linear kernels for SVMs
in Spark would be great to have. The main barriers are:
* Kernelized SVM training is hard to distribute. Naive methods require a lot
of communication. To get this feature into Spark, we'd need to do proper
background research and write up a good design.
* Other ML algorithms are arguably more in demand and still need improvements
(as of the date of this comment). Tree ensembles are first-and-foremost in my
mind.
> Spark's MLlib SVM classification to include Kernels like Gaussian / (RBF) to
> find non linear boundaries
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-4638
> URL: https://issues.apache.org/jira/browse/SPARK-4638
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: madankumar s
> Labels: Gaussian, Kernels, SVM
> Attachments: kernels-1.3.patch
>
>
> SPARK MLlib Classification Module:
> Add Kernel functionalities to SVM Classifier to find non linear patterns
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
