TY  - BOOK
AB  - 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.
DA  - 2016
LA  - eng
PY  - 2016
TI  - Modelling of Parameterized Processes via Regression in the Model Space
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29009707
Y2  - 2024-11-22T02:07:50
ER  -