Benchmarking Support Vector Machines

David Meyer, Friedrich Leisch, Kurt Hornik

Publikation: Working/Discussion PaperWU Working Paper

35 Downloads (Pure)

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.

Publikationsreihe

ReiheReport Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Nummer78

WU Working Paper Reihe

  • Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\

Zitat