Benchmarking Support Vector Machines

Meyer, David and Leisch, Friedrich and Hornik, Kurt ORCID: (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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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.

Item Type: Paper
Keywords: Maschinelles Lernen / Support-Vektor-Maschine / Klassifikator <Informatik> / Regressionsmodell
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft
Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes
Departments > Marketing > Service Marketing und Tourismus
Depositing User: Repository Administrator
Date Deposited: 04 Feb 2003 13:00
Last Modified: 24 Oct 2019 13:41


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