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https://issues.apache.org/jira/browse/SPARK-1682?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Patrick Wendell updated SPARK-1682:
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Fix Version/s: (was: 1.0.0)
> Add gradient descent w/o sampling and RDA L1 updater
> ----------------------------------------------------
>
> Key: SPARK-1682
> URL: https://issues.apache.org/jira/browse/SPARK-1682
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.0.0
> Reporter: Dong Wang
>
> The GradientDescent optimizer does sampling before a gradient step. When
> input data is already shuffled beforehand, it is possible to scan data and
> make gradient descent for each data instance. This could be potentially more
> efficient.
> Add enhanced RDA L1 updater, which could produce even sparse solutions with
> comparable quality compared with L1. Reference:
> Lin Xiao, "Dual Averaging Methods for Regularized Stochastic Learning and
> Online Optimization", Journal of Machine Learning Research 11 (2010)
> 2543-2596.
> Small fix: add options to BinaryClassification example to read and write
> model file
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