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dc.contributor.authorCzogiel, I.-
dc.contributor.authorLuebke, K.-
dc.contributor.authorWeihs, C.-
dc.date.accessioned2006-02-27T14:09:32Z-
dc.date.available2006-02-27T14:09:32Z-
dc.date.issued2006-02-27T14:09:32Z-
dc.identifier.urihttp://hdl.handle.net/2003/22205-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14252-
dc.description.abstractThe performance of an algorithm often largely depends on some hyper parameter which should be optimized before its usage. Since most conventional optimization methods suffer from some drawbacks, we developed an alternative way to find the best hyper parameter values. Contrary to the well known procedures, the new optimization algorithm is based on statistical methods since it uses a combination of Linear Mixed Effect Models and Response Surface Methodology techniques. In particular, the Method of Steepest Ascent which is well known for the case of an Ordinary Least Squares setting and a linear response surface has been generalized to be applicable for repeated measurements situations and for response surfaces of order o <= 2.en
dc.format.extent261044 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.subjectDeterministic error termsen
dc.subjectMethod of Steepest Ascenten
dc.subjectRandom Intercepts Modelen
dc.subjectRepeated measurementsen
dc.subjectSupport Vector Machineen
dc.subject.ddc004-
dc.titleResponse Surface Methodology for Optimizing Hyper Parametersen
dc.typeText-
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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