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Benchmarking Support Vector Machines

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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