Langanzeige der Metadaten
DC ElementWertSprache
dc.contributor.authorLuebke, K.-
dc.contributor.authorWeihs, Claus-
dc.date.accessioned2005-10-12T06:56:27Z-
dc.date.available2005-10-12T06:56:27Z-
dc.date.issued2005-10-12T06:56:27Z-
dc.identifier.urihttp://hdl.handle.net/2003/21642-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14499-
dc.description.abstractLinear Discriminant Analysis (LDA) performs well for classification of business phases – even though the premises of an LDA are not met. As the variables are highly correlated there are numerical as well as interpretational shortcomings. By transforming the classification problem to a regression setting both problems can be addressed by a computer-intensive prediction oriented method which also improves the classification performance.en
dc.format.extent114441 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.subjectBusiness phasesen
dc.subjectClassificationen
dc.subjectClassification performanceen
dc.subjectComputer-intensive prediction oriented methoden
dc.subjectLinear Discriminant Analysisen
dc.subject.ddc004-
dc.titlePrediction Optimal Classification of Business Phasesen
dc.typeText-
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Enthalten in den Sammlungen:Sonderforschungsbereich (SFB) 475

Dateien zu dieser Ressource:
Datei Beschreibung GrößeFormat 
tr41-05.pdfDNB111.76 kBAdobe PDFÖffnen/Anzeigen


Diese Ressource ist urheberrechtlich geschützt.



Diese Ressource ist urheberrechtlich geschützt. rightsstatements.org