[ https://issues.apache.org/jira/browse/FLINK-1729?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Till Rohrmann updated FLINK-1729: --------------------------------- Issue Type: New Feature (was: Improvement) > 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 > 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. > Resources: > [1] [http://leon.bottou.org/projects/sgd] -- This message was sent by Atlassian JIRA (v6.3.4#6332)