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• W2428540838 abstract "We consider the modelling of parameterized processes, where the goal is to model the process for new parameter value combinations. We compare the classical regression approach to a modular approach based on regression in the model space: First, for each process parametrization a model is learned. Second, a mapping from process parameters to model parameters is learned. We evaluate both approaches on a real and a synthetic dataset and show the advantages of the regression in the model space." @default.
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• W2428540838 date "2016-01-01" @default.
• W2428540838 modified "2023-09-23" @default.
• W2428540838 title "Modelling of parameterized processes via regression in the model space." @default.
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