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An alternative approach to avoid overfitting for surrogate models
2011
Proceedings of the 2011 Winter Simulation Conference (WSC)
Surrogate models are data-driven models used to accurately mimic the complex behavior of a system. They are often used to approximate computationally expensive simulation code in order to speed up the exploration of design spaces. A crucial step in the building of surrogate models is finding a good set of hyperparameters, which determine the behavior of the model. This is especially important when dealing with sparse data, as the models are in that case more prone to overfitting.
doi:10.1109/wsc.2011.6147981
dblp:conf/wsc/NguyenCKDGS11
fatcat:dol6yipqyfbbjetk447y6bdrqm