| Package | Description |
|---|---|
| org.drip.learning.kernel | |
| org.drip.learning.svm |
| Class and Description |
|---|
| DiagonalScalingOperator
DiagonalScalingOperator implements the Scaling Operator that is used to determine the Bounds of the R^x L2
To R^x L2 Kernel Linear Integral Operator defined by:
T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}
The References are:
1) Ash, R.
|
| EigenFunctionRdToR1
EigEigenFunctionRdToR1enFunction holds the Eigen-vector Function and its corresponding Space of the R^d To
R^1 Kernel Linear Integral Operator defined by:
T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}
The References are:
1) Ash, R.
|
| IntegralOperatorEigenComponent
IntegralOperatorEigenComponent holds the Eigen-Function Space and the Eigenvalue Functions/Spaces of the
R^x L2 To R^x L2 Kernel Linear Integral Operator defined by:
T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}
The References are:
1) Ash, R.
|
| IntegralOperatorEigenContainer
IntegralOperatorEigenContainer holds the Group of Eigen-Components that result from the Eigenization of
the R^x L2 To R^x L2 Kernel Linear Integral Operator defined by:
T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}
The References are:
1) Ash, R.
|
| SymmetricRdToNormedR1Kernel
SymmetricRdToNormedR1Kernel exposes the Functionality behind the Kernel that is Normed R^d X Normed R^d To
Supremum R^1, that is, a Kernel that symmetric in the Input Metric Vector Space in terms of both the
Metric and the Dimensionality.
|
| Class and Description |
|---|
| DiagonalScalingOperator
DiagonalScalingOperator implements the Scaling Operator that is used to determine the Bounds of the R^x L2
To R^x L2 Kernel Linear Integral Operator defined by:
T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}
The References are:
1) Ash, R.
|
| SymmetricRdToNormedRdKernel
SymmetricRdToNormedRdKernel exposes the Functionality behind the Kernel that is Normed R^d X Normed R^d To
Normed R^d, that is, a Kernel that symmetric in the Input Metric Vector Space in terms of both the
Metric and the Dimensionality.
|