SVM Kernels for Time Series Analysis

dc.contributor.author	Rüping, Stefan	de
dc.date.accessioned	2004-12-06T18:50:37Z
dc.date.available	2004-12-06T18:50:37Z
dc.date.issued	2001	de
dc.description.abstract	Time 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.extent	154010 bytes
dc.format.extent	91493 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5258
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15237
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	support vector machines	en
dc.subject	time series	en
dc.subject.ddc	310	de
dc.title	SVM Kernels for Time Series Analysis	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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