Christmann, Andreas2004-12-062004-12-062004http://hdl.handle.net/2003/487210.17877/DE290R-15069The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies and we investigate a general strategy which exploits the knowledge of such features. The results of the strategy are a basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a nonparametric approach based on a combination of kernel logistic regression and e-support vector regression.enUniversitätsbibliothek Dortmunddata mininginsurance tariffskernel logistic regressionmachine learningregressionrobustnesssimplicitysupport vector machinesupport vector regression310report