1. WEKA: a machine learning workbench
2. Probabilistic supervised learning, Gressmann, Frithjof and Király, Franz J. and Mateen, Bilal and Oberhauser, Harald, 2018, ArXiv, 1801.00753
3. Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard, :Users/mloning/Library/Application Support/Mendeley Desktop/Downloaded/Pedregosa et al. - 2001 - Scikit-learn Machine Learning in Python.pdf:pdf, The Journal of Machine Learning Research, 2825–2830, MIT Press, Scikit-learn: Machine Learning in Python, https://dl.acm.org/doi/10.5555/1953048.2078195, 12, 2011
4. API design for machine learning software: experiences from the scikit-learn project, Buitinck, Lars and Louppe, Gilles and Blondel, Mathieu and Pedregosa, Fabian and Mueller, Andreas and Grisel, Olivier and Niculae, Vlad and Prettenhofer, Peter and Gramfort, Alexandre and Grobler, Jaques and Layton, Robert and VanderPlas, Jacob and Joly, Arnaud and Holt, Brian and Varoquaux, Gaël, ArXiv, 2013, abs/1309.0238
5. Bischl, Bernd and Lang, Michel and Kotthoff, Lars and Schiffner, Julia and Richter, Jakob and Studerus, Erich and Casalicchio, Giuseppe and Jones, Zachary M., mlr: Machine Learning in R, Journal of Machine Learning Research, 2016, 17, 170, 1-5, http://jmlr.org/papers/v17/15-066.html