Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/3623
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dc.contributor.authorSalisu Mamman Abdulrhamanen
dc.contributor.authorPavel Brazdilen
dc.date.accessioned2017-11-20T10:49:00Z-
dc.date.available2017-11-20T10:49:00Z-
dc.date.issued2014en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/3623-
dc.description.abstractThe vast majority of studies in meta-learning uses only few performance measures when characterizing different machine learning algorithms. The measure Adjusted Ratios of Ratio (ARR) addresses the problem of how to evaluate the quality of a model based on the accuracy and training time. Unfortunately, this measure suffers from a shortcoming that is described in this paper. A new solution is proposed and it is shown that the proposed function satisfies the criterion of monotonicity, unlike ARR.en
dc.languageengen
dc.relation6144en
dc.relation5339en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleMeasures for Combining Accuracy and Time for Meta-learningen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Conferences

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