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- aggregation classification "P1".
- aggregation creator B120286.
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- aggregation creator person.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 2029398.bibtex.
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- aggregation isPartOf urn:isbn:9781457721090.
- aggregation isPartOf urn:issn:0891-7736.
- aggregation language "eng".
- aggregation publisher "IEEE".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "An alternative approach to avoid overfitting for surrogate models".
- aggregation abstract "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. Cross-validation is often used to optimize the hyperparameters of surrogate models, however it is computationally expensive and can still lead to overfitting or other erratic model behavior. This paper introduces a new auxiliary measure for the optimization of the hyperparameters of surrogate models which, when used in conjunction with a cheap accuracy measure, is fast and effective at avoiding unexplained model behavior.".
- aggregation authorList BK309054.
- aggregation endPage "2771".
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