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dc.contributor.authorWitten, Ian H.
dc.contributor.authorFrank, Eibe
dc.contributor.authorTrigg, Leonard E.
dc.contributor.authorHall, Mark A.
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorCunningham, Sally Jo
dc.date.accessioned2008-10-17T03:38:34Z
dc.date.available2008-10-17T03:38:34Z
dc.date.issued1999-08
dc.identifier.citationWitten, I.H., Frank, E., Trigg, L., Hall, M., Holmes, G. & Cunningham, S.J. (1999). Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1040
dc.description.abstractThe Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries can be run on any computer with a Web browsing capability; this allows users to apply machine learning techniques to their own data regardless of computer platform.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.titleWeka: Practical machine learning tools and techniques with Java implementationsen_US
dc.typeWorking Paperen_US
uow.relation.series99/11


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