On a combination of convex risk minimization methods

Abstract

A combination of methods from modern statistical machine learning theory based on convex risk minimization is proposed. An interesting pair for such a combination is kernel logistic regression to estimate conditional probabilities and ε-support vector regression to estimate conditional expectations. A strategy based on this combination can be helpful to detect and to model high-dimensional dependency structures in complex data sets, e.g. for constructing insurance tariffs. © Springer-Verlag Berlin, Heidelberg 2005.