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Posted to issues@spark.apache.org by "Yanbo Liang (JIRA)" <ji...@apache.org> on 2016/09/22 04:28:20 UTC
[jira] [Comment Edited] (SPARK-14709) spark.ml API for linear SVM
[ https://issues.apache.org/jira/browse/SPARK-14709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15512085#comment-15512085 ]
Yanbo Liang edited comment on SPARK-14709 at 9/22/16 4:27 AM:
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[~yuhaoyan] Any update about this? I think providing DataFrame-based SVM algorithm is very important to users, so it's better we can get it in ASAP. I'd like to get in the implementation with OWLQN and Hinge loss firstly, and to discuss SMO version later. Like [~mlnick] said, it's better to get more performance number and user case of SMO impl. And it's not very hard to add a new internal implementation after we have the basic SVM API. I saw you have an implementation with OWLQN and Hinge loss already, could you send the PR? If you are busy with other things, I can help and you are still the primary author of this PR. Thanks!
was (Author: yanboliang):
[~yuhaoyan] Any update about this? I think providing DataFrame-based SVM algorithm is very important to users, so it's better we can get it in ASAP. I'd like to get in the implementation with OWLQN and Hinge loss firstly, and to discuss SMO version later. Like [~mlnick] said, it's better to get more performance number and user case of SMO impl. And it's not very hard to add a new internal implementation after we have the basic SVM API. I saw you have a implementation with OWLQN and Hinge loss already, could you send the PR? If you are busy with other things, I can help and you are still the primary author of this PR. Thanks!
> spark.ml API for linear SVM
> ---------------------------
>
> Key: SPARK-14709
> URL: https://issues.apache.org/jira/browse/SPARK-14709
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Joseph K. Bradley
>
> Provide API for SVM algorithm for DataFrames. I would recommend using OWL-QN, rather than wrapping spark.mllib's SGD-based implementation.
> The API should mimic existing spark.ml.classification APIs.
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