WEKA: Waikato Environment for Knowledge Analysis. 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.

References in zbMATH (referenced in 305 articles , 1 standard article )

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  1. Greensmith, Julie; Feyereisl, Jan; Aickelin, Uwe: The DCA: SOMe comparison. A comparative study between two biologically inspired algorithms (2008) ioport
  2. Liu, Ying; Zhang, Dengsheng; Lu, Guojun: Region-based image retrieval with high-level semantics using decision tree learning (2008)
  3. Archetti, Francesco; Lanzeni, Stefano; Messina, Enza; Vanneschi, Leonardo: Genetic programming for computational pharmacokinetics in drug discovery and development (2007) ioport
  4. Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun: An intelligent learning diagnosis system for Web-based thematic learning platform (2007) MathEduc
  5. Bazan, Jan G.; Szczuka, Marcin: The rough set exploration system (2005)

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Further publications can be found at: http://www.cs.waikato.ac.nz/ml/publications.html