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https://issues.apache.org/jira/browse/SPARK-2309?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14953024#comment-14953024
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Sean Owen commented on SPARK-2309:
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Hm, I'm not sure I've seen a formulation like that before. Typically you have a
set of input features, and K output classes, and you learn K one-vs-all
classification boundaries. But that means the same features in each case, but
different coefficients for each class. That's what your reference says too, but
in the "Multinomial logit" section, which is what we're talking about here no?
I'm actually a little confused by
http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25 on reviewing; does
the notation change on the third line or am I missing a key step? x shouldn't
be specific to the output class k; w should be.
> Generalize the binary logistic regression into multinomial logistic regression
> ------------------------------------------------------------------------------
>
> Key: SPARK-2309
> URL: https://issues.apache.org/jira/browse/SPARK-2309
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: DB Tsai
> Assignee: DB Tsai
> Priority: Critical
> Fix For: 1.3.0
>
>
> Currently, there is no multi-class classifier in mllib. Logistic regression
> can be extended to multinomial one straightforwardly.
> The following formula will be implemented.
> http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25
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