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We propose a new radial basis function (RBF) model for stochastic simulation, called regularized RBF (R-RBF). We construct the R-RBF model by minimizing a regularized loss over a reproducing kernel Hilbert space (RKHS) associated with RBFs. The model can flexibly incorporate various types of RBFs including those with conditionally positive definite basis. To estimate the model prediction error, we...
This paper introduces an adaptive Radial Basis Function (RBF) method using weighted improvement for the global optimization of black-box problems subject to box constraints. The proposed method applies rank-one update to efficiently build RBF models and derives a closed form for the leave-one-out cross validation (LOOCV) error of RBF models, allowing an adaptive choice of radial basis functions. In...
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