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dc.contributor.authorHilbert, Andreasen_US
dc.date.accessioned2009-01-29T15:09:27Z-
dc.date.available2009-01-29T15:09:27Z-
dc.date.issued2002en_US
dc.identifier.urihttp://hdl.handle.net/10419/22816-
dc.description.abstractDecision trees are used very successfully for the identification resp. classification task of objects in many domains like marketing (e.g. Decker, Temme (2001)) or medicine. Other procedures to classify objects are for instance the logistic regression, the logit- or probit analysis, the linear or squared discriminant analysis, the nearest neighbour procedure or some kernel density estimators. The common aim of all these classification procedures is to generate classification rules which describe the correlation between some independent exogenous variables resp. attributes and at least one endogenous variable, the so called class membership variable.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesArbeitspapiere zur mathematischen Wirtschaftsforschung 180en_US
dc.subject.ddc330en_US
dc.subject.stwEntscheidungsbaumen_US
dc.subject.stwKorrelationen_US
dc.subject.stwTheorieen_US
dc.titleSome Remarks about the Usage of Asymmetric Correlation Measurements for the Induction of Decision Treesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn379948885en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:augamw:180-
Appears in Collections:Arbeitspapiere zur mathematischen Wirtschaftsforschung, Universität Augsburg

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