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dc.contributor.authorCunningham, Sally Jo
dc.contributor.authorHumphrey, Matthew C.
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-29T01:57:47Z
dc.date.available2008-10-29T01:57:47Z
dc.date.issued1996-10
dc.identifier.citationCunningham, S. J., Humphrey, M. C. & Witten, I. H. (1996). Understanding what machine learning produces - Part I: Representations and their comprehensibility. (Working paper 96/21). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1181
dc.description.abstractThe aim of many machine learning users is to comprehend the structures that are inferred from a dataset, and such users may be far more interested in understanding the structure of their data than in predicting the outcome of new test data. Part I of this paper surveys representations based on decision trees, production rules and decision graphs that have been developed and used for machine learning. These representations have differing degrees of expressive power, and particular attention is paid to their comprehensibility for non-specialist users. The graphic form in which a structure is portrayed also has a strong effect on comprehensibility, and Part II of this paper develops knowledge visualization techniques that are particularly appropriate to help answer the questions that machine learning users typically ask about the structures produced.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titleUnderstanding what machine learning produces - Part I: Representations and their comprehensibilityen_US
dc.typeWorking Paperen_US
uow.relation.series96/21


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