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dc.contributor.authorPhan, Thong H.
dc.contributor.authorWitten, Ian H.
dc.date.accessioned2008-10-23T02:48:55Z
dc.date.available2008-10-23T02:48:55Z
dc.date.issued1994-08
dc.identifier.citationPhan, T.H. & Witten, I.H. (1994). Data transformation: a semantically-based approach to function discovery. (Working paper 94/15). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1144
dc.description.abstractThis paper presents the method of data transformation for discovering numeric functions from their examples. Based on the idea of transformations between functions, this method can be viewed as a semantic counterpart to the more common approach of formula construction used in most previous discovery systems. Advantages of the new method include a flexible implementation through the design of transformation rules, and a sound basis for rigorous mathematical analysis to characterize what can be discovered. The method has been implemented in a discovery system called "LINUS," which can identify a wide range of functions: rational functions, quadratic relations, and many transcendental functions, as well as those that can be transformed to rational functions by combinations of differentiation, logarithm and function inverse operations.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.subjectFunction discoveryen_US
dc.subjectdata transformationen_US
dc.subjectMachine learning
dc.titleData transformation: a semantically-based approach to function discoveryen_US
dc.typeWorking Paperen_US
uow.relation.series94/15


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