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https://issues.apache.org/jira/browse/FLINK-1729?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Till Rohrmann reassigned FLINK-1729:
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Assignee: Till Rohrmann
> Assess performance of classification algorithms
> -----------------------------------------------
>
> Key: FLINK-1729
> URL: https://issues.apache.org/jira/browse/FLINK-1729
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Till Rohrmann
> Labels: ML
>
> In order to validate Flink's classification algorithms (in terms of
> performance and accuracy), we should run them on publicly available
> classification data sets. This will not only serve as a proof for the
> correctness of the implementations but will also show how easy the machine
> learning library can be used.
> Bottou [1] published some results for the RCV1 dataset using SVMs for
> classification. The SVMs are trained using stochastic gradient descent. Thus,
> they would be a good comparison for the CoCoA trained SVMs.
> Some more benchmark results and publicly available data sets ca be found here
> [2].
> Resources:
> [1] [http://leon.bottou.org/projects/sgd]
> [2] [https://github.com/BIDData/BIDMach/wiki/Benchmarks]
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