Meyer, David and Leisch, Friedrich and Hornik, Kurt (2002) Benchmarking Support Vector Machines. Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science", 78. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, Vienna.
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Abstract
Support Vector Machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive. (author's abstract)
| Item Type: | Paper |
|---|---|
| Keywords: | Maschinelles Lernen / Support-Vektor-Maschine / Klassifikator <Informatik> / Regressionsmodell |
| Divisions: | Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes Departments > Finance, Accounting and Statistics > Statistics and Mathematics Departments > Marketing > Tourismus und Freizeitwirtschaft Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft |
| Depositing User: | Repository Administrator |
| Date Deposited: | 04 Feb 2003 14:00 |
| Last Modified: | 14 Sep 2010 20:30 |
| WU Online Catalog: | http://onlinekatalog.wu.ac.at/F?func=find-b&reques... |
| URI: | http://epub.wu.ac.at/id/eprint/1578 |
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