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dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorHall, Mark A.
dc.date.accessioned2008-10-13T03:47:01Z
dc.date.available2008-10-13T03:47:01Z
dc.date.issued2000-07
dc.identifier.citationHolmes, G. & Hall, M.A. (2000). Correlation-based feature selection of discrete and numeric class machine learning. (Working paper 00/09). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1025
dc.description.abstractWEKA (Waikato Environment for Knowledge Analysis) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning/data mining algorithms. Non-programmers interact with the software via a user interface component called the Knowledge Explorer. Applications constructed from the WEKA class libraries can be run on any computer with a web browsing capability, allowing users to apply machine learning techniques to their own data regardless of computer platform. This paper describes the user interface component of the WEKA system in reference to previous applications in the predictive modeling of foods.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Waikato, Department of Computer Scienceen_US
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titleA development environment for predictive modelling in foodsen_US
dc.typeWorking Paperen_US
uow.relation.series00/09
pubs.elements-id55131
pubs.place-of-publicationHamiltonen_NZ


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