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dc.contributor.authorCunningham, Sally Jo
dc.coverage.spatialConference held at Ft. Lauderdale, FLen_NZ
dc.date.accessioned2008-10-29T02:58:15Z
dc.date.available2008-10-29T02:58:15Z
dc.date.issued1996-11
dc.identifier.citationCunningham, S. J. (1996). Dataset cataloging metadata for machine learning applications and research. (Working paper 96/26). Hamilton, New Zealand: University of Waikato, Department of Computer Science.en_US
dc.identifier.issn1170-487X
dc.identifier.urihttps://hdl.handle.net/10289/1187
dc.description.abstractAs the field of machine learning (ML) matures, two types of data archives are developing: collections of benchmark data sets used to test the performance of new algorithms, and data stores to which machine learning/data mining algorithms are applied to create scientific or commercial applications. At present, the catalogs of these archives are ad hoc and not tailored to machine learning analysis. This paper considers the cataloging metadata required to support these two types of repositories, and discusses the organizational support necessary for archive catalog maintenance.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesComputer Science Working Papers
dc.source6th International AI and Statistics Workshopen_NZ
dc.subjectcomputer scienceen_US
dc.titleDataset cataloging metadata for machine learning applications and researchen_US
dc.typeWorking Paperen_US
uow.relation.series96/26
pubs.elements-id24467


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