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dc.contributor.authorChristmann, Andreas-
dc.contributor.authorLuebke, Karsten-
dc.contributor.authorMarin-Galiano, Marcos-
dc.contributor.authorRüping, Stefan-
dc.date.accessioned2005-11-07T11:39:49Z-
dc.date.available2005-11-07T11:39:49Z-
dc.date.issued2005-11-07T11:39:49Z-
dc.identifier.urihttp://hdl.handle.net/2003/21667-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14494-
dc.description.abstractWe investigate methods to determine appropriate choices of the hyper-parameters for kernel based methods. Support vector classification, kernel logistic regression and support vector regression are considered. Grid search, Nelder-Mead algorithm and pattern search algorithm are used.en
dc.format.extent502994 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.subjectconvex risk minimizationen
dc.subjectkernel logistic regressionen
dc.subjectstatistical machine learningen
dc.subjectsupport vector machineen
dc.subjectsupport vector regressionen
dc.subject.ddc004-
dc.titleDetermination of hyper-parameters for kernel based classification and regressionen
dc.typeText-
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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