Authors: | Rüping, Stefan |
Title: | SVM Kernels for Time Series Analysis |
Language (ISO): | en |
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. |
Subject Headings: | support vector machines time series |
URI: | http://hdl.handle.net/2003/5258 http://dx.doi.org/10.17877/DE290R-15237 |
Issue Date: | 2001 |
Provenance: | Universitätsbibliothek Dortmund |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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43_01.pdf | DNB | 89.35 kB | Adobe PDF | View/Open |
tr43-01.ps | 150.4 kB | Postscript | View/Open |
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