Méthode | Description | |
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Distance ( double x, double y ) : double |
Computes the distance in input space between two points given in feature space.
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Estimate ( double inputs, int samples, DoubleRange &range ) : |
Estimate appropriate values for sigma given a data set. This method uses a simple heuristic to obtain appropriate values for sigma in a radial basis function kernel. The heuristic is shown by Caputo, Sim, Furesjo and Smola, "Appearance-based object recognition using SVMs: which kernel should I use?", 2002. |
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Function ( double x, double y ) : double |
Laplacian Kernel function.
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SparseLaplacian ( ) : System |
Constructs a new Laplacian Kernel
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SparseLaplacian ( double sigma ) : System |
Constructs a new Laplacian Kernel
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public Distance ( double x, double y ) : double | ||
x | double | Vector |
y | double | Vector |
Résultat | double |
public static Estimate ( double inputs, int samples, DoubleRange &range ) : |
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inputs | double | The data set. |
samples | int | The number of random samples to analyze. |
range | DoubleRange | The range of suitable values for sigma. |
Résultat |
public Function ( double x, double y ) : double | ||
x | double | Vector |
y | double | Vector |
Résultat | double |
public SparseLaplacian ( double sigma ) : System | ||
sigma | double | The sigma slope value. |
Résultat | System |