Package org.drip.learning.kernel

Statistical Learning Banach Mercer Kernels
Author:
Lakshmi Krishnamurthy
  • Class Summary
    Class Description
    DiagonalScalingOperator
    DiagonalScalingOperator implements the Scaling Operator that is used to determine the Bounds of the Rx L2 To Rx L2 Kernel Linear Integral Operator defined by: T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}

    The References are:

    Ash, R.
    EigenFunctionRdToR1
    EigenFunctionRdToR1 holds the Eigen-vector Function and its corresponding Space of the Rd To R1 Kernel Linear Integral Operator defined by: T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}

    The References are:

    Ash, R.
    HilbertSupremumKernelSpace
    HilbertSupremumKernelSpace contains the Space of Kernels S that are a Transform from the Rd L2 Hilbert To Rm LInfinity Supremum Banach Spaces.
    IntegralOperator
    IntegralOperator implements the Rx L2 To Rx L2 Mercer Kernel Integral Operator defined by: T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}

    The References are:

    Ash, R.
    IntegralOperatorEigenComponent
    IntegralOperatorEigenComponent holds the Eigen-Function Space and the Eigenvalue Functions/Spaces of the Rx L2 To Rx L2 Kernel Linear Integral Operator defined by: T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}

    The References are:

    Ash, R.
    IntegralOperatorEigenContainer
    IntegralOperatorEigenContainer holds the Group of Eigen-Components that result from the Eigenization of the Rx L2 To Rx L2 Kernel Linear Integral Operator defined by: T_k [f(.)] := Integral Over Input Space {k (., y) * f(y) * d[Prob(y)]}

    The References are:

    Ash, R.
    MercerKernel
    MercerKernel exposes the Functionality behind the Eigenized Kernel that is Normed Rx X Normed Rx To Supremum R1

    The References are:

    Ash, R.
    SymmetricRdToNormedR1Kernel
    SymmetricRdToNormedR1Kernel exposes the Functionality behind the Kernel that is Normed Rd X Normed Rd To Supremum R1, that is, a Kernel that symmetric in the Input Metric Vector Space in terms of both the Metric and the Dimensionality.
    SymmetricRdToNormedRdKernel
    SymmetricRdToNormedRdKernel exposes the Functionality behind the Kernel that is Normed Rd X Normed Rd To Normed Rd, that is, a Kernel that symmetric in the Input Metric Vector Space in terms of both the Metric and the Dimensionality.