Uses of Interface

Packages that use KernelContainer
gov.sandia.cognition.learning.algorithm.pca Provides implementations of Principle Components Analysis (PCA). 
gov.sandia.cognition.learning.algorithm.svm Provides implementations of Support Vector Machine (SVM) learning algorithms. 
gov.sandia.cognition.learning.function.categorization Provides functions that output a discrete set of categories. 
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 KernelContainer in gov.sandia.cognition.learning.algorithm.pca

Classes in gov.sandia.cognition.learning.algorithm.pca that implement KernelContainer
 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 KernelContainer in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement KernelContainer
 class AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType>
          An abstract implementation of the BudgetedKernelBinaryCategorizerLearner for online learners.
 class AbstractOnlineKernelBinaryCategorizerLearner<InputType>
          An abstract class for an online kernel binary categorizer learner.
 class Forgetron<InputType>
          An implementation of the "self-tuned" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Basic<InputType>
          An implementation of the "basic" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Greedy<InputType>
          An implementation of the "greedy" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Result<InputType>
          The result object learned by the Forgetron, which extends the DefaultKernelBinaryCategorizer with some additional state information needed in the update step.
 class KernelBinaryCategorizerOnlineLearnerAdapter<InputType>
          A wrapper class for a KernelizableBinaryCategorizerOnlineLearner that allows it to be used as a batch or incremental learner over the input type directly, rather than using utility methods.
 class OnlineKernelPerceptron<InputType>
          An implementation of the online version of the Perceptron algorithm.
 class OnlineKernelRandomizedBudgetPerceptron<InputType>
          An implementation of a fixed-memory kernel Perceptron algorithm.
 class Projectron<InputType>
          An implementation of the Projectron algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
static class Projectron.LinearSoftMargin<InputType>
          An implementation of the Projectron++ algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
 class RemoveOldestKernelPerceptron<InputType>
          A budget kernel Perceptron that always removes the oldest item.
 class Stoptron<InputType>
          An online, budgeted, kernel version of the Perceptron algorithm that stops learning once it has reached its budget.

Uses of KernelContainer in gov.sandia.cognition.learning.algorithm.svm

Classes in gov.sandia.cognition.learning.algorithm.svm that implement KernelContainer
 class SequentialMinimalOptimization<InputType>
          An implementation of the Sequential Minimal Optimization (SMO) algorithm for training a Support Vector Machine (SVM), which is a kernel-based binary categorizer.

Uses of KernelContainer in gov.sandia.cognition.learning.function.categorization

Classes in gov.sandia.cognition.learning.function.categorization that implement KernelContainer
 class DefaultKernelBinaryCategorizer<InputType>
          A default implementation of the KernelBinaryCategorizer that uses the standard way of representing the examples (supports) using a DefaultWeightedValue.
 class KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>>
          The KernelBinaryCategorizer class implements a binary categorizer that uses a kernel to do its categorization.

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

Classes in gov.sandia.cognition.learning.function.kernel that implement KernelContainer
 class DefaultKernelContainer<InputType>
          The DefaultKernelContainer class implements an object that contains a kernel inside.
 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.

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

Classes in gov.sandia.cognition.learning.function.scalar that implement KernelContainer
 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 KernelContainer in gov.sandia.cognition.statistics.bayesian

Classes in gov.sandia.cognition.statistics.bayesian that implement KernelContainer
 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.