Kernel Class |
Namespace: Accord.Statistics.Kernels
The Kernel type exposes the following members.
Name | Description | |
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Center(Double, Double) |
Centers the given kernel matrix K.
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Center(Double, Double, Double, Double) |
Centers the given kernel matrix K.
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Center(Double, Double, Double, Double) |
Centers the given kernel matrix K.
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Center(Double, Double, Double, Double) |
Centers the given kernel matrix K.
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DistancesT(T, Double, Int32) |
Computes the set of all distances between
all points in a random subset of the data.
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DistancesTKernel, TData(TKernel, TData, Int32) |
Computes the set of all distances between
all points in a random subset of the data.
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EstimateComplexity(Double) |
Estimates the complexity parameter C, present in many SVM algorithms,
for a given kernel and a given data set by summing every element
on the diagonal of the kernel matrix and using an heuristic based
on it.
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EstimateComplexity(Double, Int32) |
Estimates the complexity parameter C, present in many SVM algorithms,
for a given kernel and an unbalanced data set by summing every element
on the diagonal of the kernel matrix and using an heuristic based on it.
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EstimateComplexityTKernel, TInput(TKernel, TInput) |
Estimates the complexity parameter C, present in many SVM algorithms,
for a given kernel and a given data set by summing every element
on the diagonal of the kernel matrix and using an heuristic based
on it.
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EstimateComplexityTKernel, TInput(TKernel, TInput, Int32) |
Estimates the complexity parameter C, present in many SVM algorithms,
for a given kernel and an unbalanced data set by summing every element
on the diagonal of the kernel matrix and using an heuristic based on it.
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ToJaggedTKernel, TInput |
Creates the Gram matrix from the given vectors.
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ToJagged2TKernel, TInput |
Creates the Gram matrix containing all dot products in feature
(kernel) space between each vector in x
and the ones in y.
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ToMatrixTKernel, TInput |
Creates the Gram matrix from the given vectors.
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ToMatrix2TKernel, TInput |
Creates the Gram matrix containing all dot products in feature
(kernel) space between each vector in x
and the ones in y.
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