Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Model Selection and Error Estimation
Machine Learning
On different facets of regularization theory
Neural Computation
Comparison of model selection for regression
Neural Computation
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Extensions to metric based model selection
The Journal of Machine Learning Research
Triangulations and Applications (Mathematics and Visualization)
Triangulations and Applications (Mathematics and Visualization)
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
IEEE Transactions on Evolutionary Computation
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
The Journal of Machine Learning Research
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
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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.