Open-source machine learning: R meets Weka. The Waikato Environment for Knowleage Analysis (Weka) is an open-source project in machine learning covering classification, regression, clustering, association rules and visualization. It is implemented on Java and released under GPL. This paper is devoted to the Weka interface for R-software provided by the R extension package RWeka. The interfacing methodology, limitations and possible extensions are discussed.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Hothorn, Torsten: partykit: a modular toolkit for recursive partytioning in $\mathsf R$ (2015)
- Henelius, Andreas; Puolamäki, Kai; Boström, Henrik; Asker, Lars; Papapetrou, Panagiotis: A peek into the black box: exploring classifiers by randomization (2014)
- Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
- Mehmood, Rashid; Riaz, Muhammad; Does, Ronald J.M.M.: Efficient power computation for $r$ out of $m$ runs rules schemes (2013)
- Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
- Urbanek, Simon: How to talk to strangers: ways to leverage connectivity between R, Java and Objective C (2009)