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https://issues.apache.org/jira/browse/SPARK-3255?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14900114#comment-14900114
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Henry Lin edited comment on SPARK-3255 at 9/20/15 11:41 PM:
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This paper here...
http://jmlr.csail.mit.edu/proceedings/papers/v28/gopal13.pdf
... has some insight. The paper experiments with different optimization methods
for distributed training, and determines that a Log-concavity bound, discovered
by David Blei and John Lafferty (2006), scales the best on large datasets.
was (Author: hlin117):
This paper <a
href="http://jmlr.csail.mit.edu/proceedings/papers/v28/gopal13.pdf";>here</a>
has some insight. The paper experiments with different optimization methods for
distributed training, and determines that a Log-concavity bound, discovered by
David Blei and John Lafferty (2006), scales the best on large datasets.
> Faster algorithms for logistic regression
> -----------------------------------------
>
> Key: SPARK-3255
> URL: https://issues.apache.org/jira/browse/SPARK-3255
> Project: Spark
> Issue Type: Brainstorming
> Components: MLlib
> Reporter: Xiangrui Meng
>
> Logistic regression is perhaps the most widely used classification algorithm
> in industry. We are looking for faster and scalable algorithms for MLlib. We
> currently have LogisticRegressionWithLBFGS, and the LIBLINEAR group
> implemented Spark LIBLINEAR:
> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/spark/running_spark_liblinear.html
> Welcome to join the discussion and add more candidates.
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