jmlr jmlr2010 jmlr2010-116 jmlr2010-116-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten
Abstract: WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project. Keywords: machine learning software, open source software
J. Bacon. The Art of Community. O’Reilly Media, 2009. W. W. Cohen. Fast effective rule induction. In Proc 12th International Conference on Machine Learning, pages 115–123. Morgan Kaufmann, 1995. E. Frank and I. H. Witten. Generating accurate rule sets without global optimization. In Proc 15th International Conference on Machine Learning, pages 144–151. Morgan Kaufmann, 1998. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: An update. SIGKDD Explorations, 11(1):10–18, 2009. R. Kohavi, D. Sommerfield, and J. Dougherty. Data mining using MLC++ a machine learning library in C++. International Journal on Artificial Intelligence Tools, 6(4):537–566, 1997. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1992. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2009. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2nd edition, 2005. 2541