Werner Hauptmann

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Real-world applications often require the joint use of data-driven and knowledge-based models. While data-driven models are learned from available process data, knowledge-based models are able to provide additional information not contained in the data. In this contribution, we propose a method to divide the input space on the basis of the validity ranges(More)
In this paper we present the challenging problem of realizing the Urban Computing vision and in particular we describe the requirements for future mobility management systems. We show that novel multidisciplinary ideas are required to address the Urban Computing challenge and that only partial solutions can be found today. The Urban Computing challenge is(More)
This paper discusses a machine learning approach for binary classification problems which satisfies the specific requirements of safety-related applications. The approach is based on ensembles of local models. Each local model utilizes only a small subspace of the complete input space. This ensures the interpretability and verifiability of the local models,(More)
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