dc.contributor.author | Holmes, Geoffrey | |
dc.contributor.author | Donkin, Andrew | |
dc.contributor.author | Witten, Ian H. | |
dc.date.accessioned | 2008-10-23T01:54:10Z | |
dc.date.available | 2008-10-23T01:54:10Z | |
dc.date.issued | 1994-07 | |
dc.identifier.citation | 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. | en_US |
dc.identifier.issn | 1170-487X | |
dc.identifier.uri | https://hdl.handle.net/10289/1138 | |
dc.description.abstract | 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. | en_US |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.relation.ispartofseries | Computer Science Working Papers | |
dc.subject | computer science | en_US |
dc.subject | Machine learning | |
dc.title | WEKA: a machine learning workbench | en_US |
dc.type | Working Paper | en_US |
uow.relation.series | 94/09 | |