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The training and probe data were identical to the proposed method, and we used a linear function as a kernel function.
We used the radial-basis function as a kernel function.
Integral type non-local elastic models phenomenologically incorporate microstructure information through a weighting function known as a kernel function.
In this study, Radial Basis Function (RBF) is preferred as a kernel function due to its flexibility to observations from many divers fields.
The SVM classifier is designed by selecting Gaussian RBF kernel as a kernel function.
First, we introduce an LCSS kernel as a kernel function that does not satisfy Mercer's theorem.
As a kernel function we used the radial basis function (RBF).
Although the choice of the kernel function is arbitrary, as any positive-definite function can be used as a kernel function, multiple factors may affect its choice in practice.
In this paper, the following radial basis function (RBF) is used as a kernel function: (8) K (x i, x i ′ ) = e − γ | x i − x i ′ | 2, where γ is the kernel parameter.
As a kernel function, a radial basis function K (x i, x j ) = exp (− γ ‖ x i − x j ‖ 2 ) with parameter γ = 10, 1, 0.1, 0.01, 0.001 is used.
The similarity measure is usually regarded as a kernel function between two feature vectors.
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