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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorDonkin, Andrew
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-23T01:54:10Z
dc.date.available2008-10-23T01:54:10Z
dc.date.issued1994-07
dc.identifier.citationHolmes, 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.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1138
dc.description.abstractWeka 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.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titleWEKA: a machine learning workbenchen_US
dc.typeWorking Paperen_US
uow.relation.series94/09


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