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dc.contributor.authorGarner, Stephen R.
dc.contributor.authorCunningham, Sally Jo
dc.contributor.authorHolmes, Geoffrey
dc.contributor.authorNevill-Manning, Craig G.
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-20T23:03:54Z
dc.date.available2008-10-20T23:03:54Z
dc.date.issued1995-05
dc.identifier.citationGarner, S. R., Cunningham, S. J., Holmes, G., Nevill-Manning, C. G. & Witten, I. H. (1995). Machine learning in practice: experience with agricultural databases. (Working paper 95/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1091
dc.description.abstractThe Waikato Environment for Knowledge Analysis (weka) is a New Zealand government-sponsored initiative to investigate the application of machine learning to economically important problems in the agricultural industries. The overall goals are to create a workbench for machine learning, determine the factors that contribute towards its successful application in the agricultural industries, and develop new methods of machine learning and ways of assessing their effectiveness. The project began in 1993 and is currently working towards the fulfilment of three objectives: to design and implement the workbench, to provide case studies of applications of machine learning techniques to problems in agriculture, and to develop a methodology for evaluating generalisations in terms of their entropy. These three objectives are by no means independent. For example, the design of the weka workbench has been inspired by the demands placed on it by the case studies, and has also benefited from our work on evaluating the outcomes of applying a technique to data. Our experience throughout the development of the project is that the successful application of machine learning involves much more than merely executing a learning algorithm on some data. In this paper we present the process model that underpins our work over the past two years for the development of applications in agriculture; the software we have developed around our workbench of machine learning schemes to support this model; and the outcomes and problems we have encountered in developing applications.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.titleMachine learning in practice: experience with agricultural databasesen_US
dc.typeWorking Paperen_US
uow.relation.series95/13


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