@techreport{3a457d646502470dafc3d59465eaa7f8,
title = "Benchmarking Support Vector Machines",
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 = "David Meyer and Friedrich Leisch and Kurt Hornik",
year = "2002",
language = "English",
series = "Report Series SFB {"}Adaptive Information Systems and Modelling in Economics and Management Science{"}",
number = "78",
publisher = "SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business",
edition = "November 2002",
type = "WorkingPaper",
institution = "SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business",
}