Holmes, G., Donkin, A. & Witten, I.H. (1994). WEKA: a machine learning workbench. (Working paper 94/09). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Permanent Research Commons link: http://hdl.handle.net/10289/1138
Weka is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learned include the necessity of providing a wealth of interactive tools for data manipulation, result visualization, database linkage, and cross-validation and comparison of rule sets, to complement the basic machine learning tools.
- 1994 Working Papers