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Posted to dev@flink.apache.org by "Till Rohrmann (JIRA)" <ji...@apache.org> on 2015/03/18 15:12:38 UTC

[jira] [Created] (FLINK-1729) Assess performance of classification algorithms

Till Rohrmann created FLINK-1729:
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             Summary: Assess performance of classification algorithms
                 Key: FLINK-1729
                 URL: https://issues.apache.org/jira/browse/FLINK-1729
             Project: Flink
          Issue Type: Improvement
          Components: Machine Learning Library
            Reporter: Till Rohrmann


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.

Resources:
[1] [http://leon.bottou.org/projects/sgd]



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