Uses of Class
gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer

Packages that use DefaultKernelContainer
gov.sandia.cognition.learning.algorithm.pca Provides implementations of Principle Components Analysis (PCA). 
gov.sandia.cognition.learning.function.kernel Provides kernel functions. 
gov.sandia.cognition.learning.function.scalar Provides functions that output real numbers. 
gov.sandia.cognition.statistics.bayesian Provides algorithms for computing Bayesian estimates of parameters. 
 

Uses of DefaultKernelContainer in gov.sandia.cognition.learning.algorithm.pca
 

Subclasses of DefaultKernelContainer in gov.sandia.cognition.learning.algorithm.pca
 class KernelPrincipalComponentsAnalysis<DataType>
          An implementation of the Kernel Principal Components Analysis (KPCA) algorithm.
static class KernelPrincipalComponentsAnalysis.Function<DataType>
          The resulting transformation function learned by Kernel Principal Components Analysis.
 

Uses of DefaultKernelContainer in gov.sandia.cognition.learning.function.kernel
 

Subclasses of DefaultKernelContainer in gov.sandia.cognition.learning.function.kernel
 class ExponentialKernel<InputType>
          The ExponentialKernel class implements a kernel that applies the exponential function to the result of another kernel.
 class KernelDistanceMetric<InputType>
          The KernelDistanceMetric class implements a distance metric that utilizes an underlying Kernel for computing the distance.
 class NormalizedKernel<InputType>
          The NormalizedKernel class implements an Kernel that returns a normalized value between 0.0 and 1.0 by normalizing the results of a given kernel.
 class VectorFunctionKernel
          The VectorFunctionKernel implements a kernel that makes use of a vector function plus a kernel that operates on vectors.
 class WeightedKernel<InputType>
          The WeightedKernel class implements a kernel that takes another kernel, evaluates it, and then the result is rescaled by a given weight.
 

Methods in gov.sandia.cognition.learning.function.kernel that return DefaultKernelContainer
 DefaultKernelContainer<InputType> DefaultKernelContainer.clone()
           
 

Constructors in gov.sandia.cognition.learning.function.kernel with parameters of type DefaultKernelContainer
DefaultKernelContainer(DefaultKernelContainer<? super InputType> other)
          Creates a new copy of a KernelContainer and the kernel inside.
 

Uses of DefaultKernelContainer in gov.sandia.cognition.learning.function.scalar
 

Subclasses of DefaultKernelContainer in gov.sandia.cognition.learning.function.scalar
 class KernelScalarFunction<InputType>
          The KernelScalarFunction class implements a scalar function that uses a kernel to compute its output value.
 class LocallyWeightedKernelScalarFunction<InputType>
          The LocallyWeightedKernelScalarFunction class implements a scalar function that uses kernels and does local weighting on them to get the result value.
 

Uses of DefaultKernelContainer in gov.sandia.cognition.statistics.bayesian
 

Subclasses of DefaultKernelContainer in gov.sandia.cognition.statistics.bayesian
 class GaussianProcessRegression<InputType>
          Gaussian Process Regression, is also known as Kriging, is a nonparametric method to interpolate and extrapolate using Bayesian regression, where the expressiveness of the estimator can grow with the data.