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https://issues.apache.org/jira/browse/SPARK-5256?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14494568#comment-14494568
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Alexander Ulanov edited comment on SPARK-5256 at 4/14/15 6:43 PM:
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The size of data that requires to use Spark suggests that learning algorithm
will be limited by time versus data. According to the paper "The tradeoffs of
large scale learning", SGD has significantly faster convergence than batch GD
in this case. My use case is machine learning on large data, in particular,
time series.
Just in case, link to the paper
http://papers.nips.cc/paper/3323-the-tradeoffs-of-large-scale-learning.pdf
was (Author: avulanov):
The size of data that requires to use Spark suggests that learning algorithm
will be limited by time versus data. According to the paper "The tradeoffs of
large scale learning", SGD has significantly faster convergence than batch GD
in this case. My use case is machine learning on large data, in particular,
time series.
> Improving MLlib optimization APIs
> ---------------------------------
>
> Key: SPARK-5256
> URL: https://issues.apache.org/jira/browse/SPARK-5256
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> *Goal*: Improve APIs for optimization
> *Motivation*: There have been several disjoint mentions of improving the
> optimization APIs to make them more pluggable, extensible, etc. This JIRA is
> a place to discuss what API changes are necessary for the long term, and to
> provide links to other relevant JIRAs.
> Eventually, I hope this leads to a design doc outlining:
> * current issues
> * requirements such as supporting many types of objective functions,
> optimization algorithms, and parameters to those algorithms
> * ideal API
> * breakdown of smaller JIRAs needed to achieve that API
> I will soon create an initial design doc, and I will try to watch this JIRA
> and include ideas from JIRA comments.
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