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.
This work has been supported by the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 475.
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Christmann, A. (2005). On a Combination of Convex Risk Minimization Methods. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_50
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DOI: https://doi.org/10.1007/3-540-28084-7_50
Publisher Name: Springer, Berlin, Heidelberg
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