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 |