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dc.contributor.authorRüping, Stefande
dc.date.accessioned2004-12-06T18:50:37Z-
dc.date.available2004-12-06T18:50:37Z-
dc.date.issued2001de
dc.identifier.urihttp://hdl.handle.net/2003/5258-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15237-
dc.description.abstractTime series analysis is an important and complex problem in machine learning and statistics. Real-world applications can consist of very large and high dimensional time series data. Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM kernel functions and disusses their relative merits, depending on the type of data that is used.en
dc.format.extent154010 bytes-
dc.format.extent91493 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectsupport vector machinesen
dc.subjecttime seriesen
dc.subject.ddc310de
dc.titleSVM Kernels for Time Series Analysisen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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