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The optimal subset of the basis function is selected with the global optimization algorithm; which can accurately represent the trend of true response surface.
A radial basis function is selected as kernel for this binary classifier and parameter tuning is implemented with a 5-fold cross-validation loop on the training data (Van Landeghem et al., 2010).
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In the SVM model, a Gaussian radial basis function was selected as the kernel function with an error penalty factor (C = 1) and a scaling factor (σ = 0.01).
In explicit selection methods the basis functions are selected from a set of candidates with a search process.
Various wavelet basis functions were selected in wavelet applications.
This 17 basis functions could be compared to the result for a multicarrier equalizer in [4], where at least 30 basis functions were selected by using a different approach that resorts to enumerating the contribution at certain frequencies.
As the kernels, four kinds of kernel functions, radial basis function (RBF), polynomial function, linear function, and sigmoid function, were selected to implement the experiment.
By comparing the performances of multiple kernel functions using cross validation on training set, radial basis function (RBF) is selected as the kernel function used in this simulation.
Gaussian radial basis function (RBF) is selected as kernel function because it gives an acceptable accuracy with less difficulty for its implementation (Kaytez et al. 2015).
It shows that the accuracy of the basis function is different and the best one is selected for approximate the objective and constraint functions.
The radial basis function is a deterministic interpolation that fits selected spatial functions to better describe the observation data (Bishop 1995).
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