Modelling of Parameterized Processes via Regression in the Model Space
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.
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