Uses of Interface
gov.sandia.cognition.learning.function.kernel.Kernel

Packages that use Kernel

Package	Description
gov.sandia.cognition.learning.algorithm.pca	Provides implementations of Principle Components Analysis (PCA).
gov.sandia.cognition.learning.algorithm.perceptron	Provides the Perceptron algorithm and some of its variations.
gov.sandia.cognition.learning.algorithm.perceptron.kernel
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.
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 Kernel in gov.sandia.cognition.learning.algorithm.pca

Constructors in gov.sandia.cognition.learning.algorithm.pca with parameters of type Kernel

Constructor and Description
Function(Kernel<? super DataType> kernel, java.util.List<? extends DataType> data, Matrix components, boolean centerData, Matrix kernelMatrix) Creates a new Kernel Principal Components Analysis function.
KernelPrincipalComponentsAnalysis(Kernel<? super DataType> kernel, int componentCount) Creates a new Kernel Principal Components Analysis with the given kernel and component count.
KernelPrincipalComponentsAnalysis(Kernel<? super DataType> kernel, int componentCount, boolean centerData) Creates a new Kernel Principal Components Analysis with the given kernel and component count.

Uses of Kernel in gov.sandia.cognition.learning.algorithm.perceptron

Methods in gov.sandia.cognition.learning.algorithm.perceptron with parameters of type Kernel

Modifier and Type	Method and Description
<InputType> DefaultKernelBinaryCategorizer<InputType>	AbstractKernelizableBinaryCategorizerOnlineLearner.createInitialLearnedObject(Kernel<? super InputType> kernel)
<InputType> DefaultKernelBinaryCategorizer<InputType>	AbstractLinearCombinationOnlineLearner.createInitialLearnedObject(Kernel<? super InputType> kernel)
<InputType> DefaultKernelBinaryCategorizer<InputType>	KernelizableBinaryCategorizerOnlineLearner.createInitialLearnedObject(Kernel<? super InputType> kernel) Creates the initial learned object with a given kernel.
<InputType> SupervisedBatchAndIncrementalLearner<InputType,java.lang.Boolean,DefaultKernelBinaryCategorizer<InputType>>	AbstractKernelizableBinaryCategorizerOnlineLearner.createKernelLearner(Kernel<? super InputType> kernel)
<InputType> SupervisedBatchAndIncrementalLearner<InputType,java.lang.Boolean,DefaultKernelBinaryCategorizer<InputType>>	KernelizableBinaryCategorizerOnlineLearner.createKernelLearner(Kernel<? super InputType> kernel) Creates a new kernel-based learner using the standard learning interfaces based on this learner and its parameters.
<InputType> DefaultKernelBinaryCategorizer<InputType>	AbstractKernelizableBinaryCategorizerOnlineLearner.learn(Kernel<? super InputType> kernel, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data)
<InputType> DefaultKernelBinaryCategorizer<InputType>	KernelizableBinaryCategorizerOnlineLearner.learn(Kernel<? super InputType> kernel, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data) Run this algorithm on a batch of data using the given kernel function.

Uses of Kernel in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Fields in gov.sandia.cognition.learning.algorithm.perceptron.kernel declared as Kernel

Modifier and Type	Field and Description
protected Kernel<? super InputType>	AbstractOnlineKernelBinaryCategorizerLearner.kernel The kernel to use.

Methods in gov.sandia.cognition.learning.algorithm.perceptron.kernel that return Kernel

Modifier and Type	Method and Description
Kernel<? super InputType>	AbstractOnlineKernelBinaryCategorizerLearner.getKernel()
Kernel<? super InputType>	KernelAdatron.getKernel() Gets the kernel to use.
Kernel<? super InputType>	KernelPerceptron.getKernel() Gets the kernel to use.

Methods in gov.sandia.cognition.learning.algorithm.perceptron.kernel with parameters of type Kernel

Modifier and Type	Method and Description
void	AbstractOnlineKernelBinaryCategorizerLearner.setKernel(Kernel<? super InputType> kernel) Sets the kernel used by this learner.
void	KernelAdatron.setKernel(Kernel<? super InputType> kernel) Sets the kernel to use.
void	KernelPerceptron.setKernel(Kernel<? super InputType> kernel) Sets the kernel to use.

Constructors in gov.sandia.cognition.learning.algorithm.perceptron.kernel with parameters of type Kernel

Constructor and Description
AbstractOnlineBudgetedKernelBinaryCategorizerLearner(Kernel<? super InputType> kernel, int budget) Creates a new AbstractOnlineBudgetedKernelBinaryCategorizerLearner with the given parameters.
AbstractOnlineKernelBinaryCategorizerLearner(Kernel<? super InputType> kernel) Creates a new AbstractOnlineKernelBinaryCategorizerLearner with the given kernel.
Basic(Kernel<? super InputType> kernel, int budget) Creates a new Forgetron.Basic with the given kernel and budget.
Forgetron(Kernel<? super InputType> kernel, int budget) Creates a new Forgetron with the given kernel and budget.
Greedy(Kernel<? super InputType> kernel, int budget) Creates a new Forgetron.Greedy with the given kernel and budget.
KernelAdatron(Kernel<? super InputType> kernel) Creates a new KernelAdatron with the given kernel.
KernelAdatron(Kernel<? super InputType> kernel, int maxIterations) Creates a new KernelAdatron with the given kernel and maximum number of iterations.
KernelBinaryCategorizerOnlineLearnerAdapter(Kernel<? super InputType> kernel, KernelizableBinaryCategorizerOnlineLearner learner) Creates a new KernelBinaryCategorizerOnlineLearnerAdapter with the given kernel and learner.
KernelPerceptron(Kernel<? super InputType> kernel) Creates a new KernelPerceptron with the given kernel.
KernelPerceptron(Kernel<? super InputType> kernel, int maxIterations) Creates a new KernelPerceptron with the given kernel and maximum number of iterations.
KernelPerceptron(Kernel<? super InputType> kernel, int maxIterations, double marginPositive, double marginNegative) Creates a new KernelPerceptron with the given parameters.
LinearSoftMargin(Kernel<? super InputType> kernel) Creates a new Projectron.LinearSoftMargin with the given kernel and default parameters.
LinearSoftMargin(Kernel<? super InputType> kernel, double eta) Creates a new Projectron.LinearSoftMargin with the given parameters.
OnlineKernelPerceptron(Kernel<? super InputType> kernel) Creates a new OnlineKernelPerceptron with the given kernel.
OnlineKernelRandomizedBudgetPerceptron(Kernel<? super InputType> kernel, int budget, java.util.Random random) Creates a new OnlineKernelRandomizedBudgetPerceptron with the given parameters.
Projectron(Kernel<? super InputType> kernel) Creates a new Projectron with the given kernel and default parameters.
Projectron(Kernel<? super InputType> kernel, double eta) Creates a new Projectron with the given parameters.
RemoveOldestKernelPerceptron(Kernel<? super InputType> kernel, int budget) Creates a new RemoveOldestKernelPerceptron with the given parameters.
Result(Kernel<? super InputType> kernel) Creates a new Result with the given kernel.
Stoptron(Kernel<? super InputType> kernel, int budget) Creates a new Stoptron with the given parameters.

Uses of Kernel in gov.sandia.cognition.learning.algorithm.regression

Methods in gov.sandia.cognition.learning.algorithm.regression that return Kernel

Modifier and Type	Method and Description
Kernel<? super InputType>	KernelBasedIterativeRegression.getKernel() Gets the kernel to use.
Kernel<? super InputType>	LocallyWeightedFunction.getKernel() Getter for kernel
Kernel<? super InputType>	LocallyWeightedFunction.Learner.getKernel() Getter for kernel
Kernel<? super OutputType>	KernelWeightedRobustRegression.getKernelWeightingFunction() Getter for kernelWeightingFunction

Methods in gov.sandia.cognition.learning.algorithm.regression with parameters of type Kernel

Modifier and Type	Method and Description
void	KernelBasedIterativeRegression.setKernel(Kernel<? super InputType> kernel) Sets the kernel to use.
void	LocallyWeightedFunction.setKernel(Kernel<? super InputType> kernel) Setter for kernel
void	LocallyWeightedFunction.Learner.setKernel(Kernel<? super InputType> kernel) Setter for kernel
void	KernelWeightedRobustRegression.setKernelWeightingFunction(Kernel<? super OutputType> kernelWeightingFunction) Getter for kernelWeightingFunction

Constructors in gov.sandia.cognition.learning.algorithm.regression with parameters of type Kernel

Constructor and Description
KernelBasedIterativeRegression(Kernel<? super InputType> kernel) Creates a new KernelBasedIterativeRegression with the given kernel.
KernelBasedIterativeRegression(Kernel<? super InputType> kernel, double minSensitivity) Creates a new KernelBasedIterativeRegression with the given kernel.
KernelBasedIterativeRegression(Kernel<? super InputType> kernel, double minSensitivity, int maxIterations) Creates a new KernelBasedIterativeRegression with the given kernel and maximum number of iterations.
KernelWeightedRobustRegression(SupervisedBatchLearner<InputType,OutputType,?> iterationLearner, Kernel<? super OutputType> kernelWeightingFunction) Creates a new instance of RobustRegression
KernelWeightedRobustRegression(SupervisedBatchLearner<InputType,OutputType,?> iterationLearner, Kernel<? super OutputType> kernelWeightingFunction, int maxIterations, double tolerance) Creates a new instance of RobustRegression
Learner(Kernel<? super InputType> kernel, SupervisedBatchLearner<InputType,OutputType,?> learner) Creates a new instance of LocallyWeightedFunction
LocallyWeightedFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> rawData, SupervisedBatchLearner<InputType,OutputType,?> learner) Evaluator that implements the concept of LocallyWeightedLearning.

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

Fields in gov.sandia.cognition.learning.algorithm.svm declared as Kernel

Modifier and Type	Field and Description
protected Kernel<? super InputType>	SuccessiveOverrelaxation.kernel The kernel to use.

Methods in gov.sandia.cognition.learning.algorithm.svm that return Kernel

Modifier and Type	Method and Description
Kernel<? super InputType>	SequentialMinimalOptimization.getKernel()
Kernel<? super InputType>	SuccessiveOverrelaxation.getKernel() Gets the kernel to use.

Methods in gov.sandia.cognition.learning.algorithm.svm with parameters of type Kernel

Modifier and Type	Method and Description
void	SequentialMinimalOptimization.setKernel(Kernel<? super InputType> kernel) Sets the kernel to use in training the SVM.
void	SuccessiveOverrelaxation.setKernel(Kernel<? super InputType> kernel) Sets the kernel to use.

Constructors in gov.sandia.cognition.learning.algorithm.svm with parameters of type Kernel

Constructor and Description
SequentialMinimalOptimization(Kernel<? super InputType> kernel) Creates a new instance of Sequential Minimal Optimization with the given kernel.
SequentialMinimalOptimization(Kernel<? super InputType> kernel, double maxPenalty, double errorTolerance, double effectiveZero, int kernelCacheSize, int maxIterations, java.util.Random random) Creates a new instance of Sequential Minimal Optimization with the given kernel and random number generator.
SequentialMinimalOptimization(Kernel<? super InputType> kernel, java.util.Random random) Creates a new instance of Sequential Minimal Optimization with the given kernel and random number generator.
SuccessiveOverrelaxation(Kernel<? super InputType> kernel) Creates a new instance of SuccessiveOverrelaxation.
SuccessiveOverrelaxation(Kernel<? super InputType> kernel, double maxWeight, double overrelaxation, double minChange, int maxIterations) Creates a new instance of SuccessiveOverrelaxation.

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

Fields in gov.sandia.cognition.learning.function.categorization declared as Kernel

Modifier and Type	Field and Description
protected Kernel<? super InputType>	KernelBinaryCategorizer.kernel The internal kernel.

Methods in gov.sandia.cognition.learning.function.categorization that return Kernel

Modifier and Type	Method and Description
Kernel<? super InputType>	KernelBinaryCategorizer.getKernel()

Methods in gov.sandia.cognition.learning.function.categorization with parameters of type Kernel

Modifier and Type	Method and Description
void	KernelBinaryCategorizer.setKernel(Kernel<? super InputType> kernel) Sets the internal kernel.

Constructors in gov.sandia.cognition.learning.function.categorization with parameters of type Kernel

Constructor and Description
DefaultKernelBinaryCategorizer(Kernel<? super InputType> kernel) Creates a new DefaultKernelBinaryCategorizer with the given kernel, no examples, and a zero bias.
DefaultKernelBinaryCategorizer(Kernel<? super InputType> kernel, java.util.Collection<DefaultWeightedValue<InputType>> examples, double bias) Creates a new DefaultKernelBinaryCategorizer with the given parameters.
KernelBinaryCategorizer(Kernel<? super InputType> kernel) Creates a new instance of KernelBinaryCategorizer with the given kernel.
KernelBinaryCategorizer(Kernel<? super InputType> kernel, java.util.Collection<EntryType> examples, double bias) Creates a new instance of KernelBinaryCategorizer with the given kernel, weighted examples, and bias.

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

Classes in gov.sandia.cognition.learning.function.kernel that implement Kernel

Modifier and Type	Class and Description
class	ExponentialKernel<InputType> The ExponentialKernel class implements a kernel that applies the exponential function to the result of another kernel.
class	LinearKernel The LinearKernel class implements the most basic kernel: it just does the actual inner product between two vectors.
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	PolynomialKernel The PolynomialKernel class implements a kernel for two given vectors that is the polynomial function: (x dot y + c)^d d is the degree of the polynomial, which must be a positive integer.
class	ProductKernel<InputType> The ProductKernel class implements a kernel that takes the product of applying multiple kernels to the same pair of inputs.
class	RadialBasisKernel The RadialBasisKernel implements the standard radial basis kernel, which is: exp( -||x - y||^2 / (2 * sigma^2) ) where sigma is the parameter that controls the bandwidth of the kernel.
class	ScalarFunctionKernel<InputType> The ScalarFunctionKernel class implements a kernel that applies a scalar function two the two inputs to the kernel and then returns their product.
class	SigmoidKernel The SigmoidKernel class implements a sigmoid kernel based on the hyperbolic tangent.
class	SumKernel<InputType> The SumKernel class implements a kernel that adds together the result of applying multiple kernels to the same pair of inputs.
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.
class	ZeroKernel The ZeroKernel always returns zero.

Fields in gov.sandia.cognition.learning.function.kernel declared as Kernel

Modifier and Type	Field and Description
protected Kernel<? super InputType>	DefaultKernelContainer.kernel The internal kernel.

Fields in gov.sandia.cognition.learning.function.kernel with type parameters of type Kernel

Modifier and Type	Field and Description
protected java.util.Collection<? extends Kernel<? super InputType>>	DefaultKernelsContainer.kernels The collection of kernels in the container.

Methods in gov.sandia.cognition.learning.function.kernel that return Kernel

Modifier and Type	Method and Description
Kernel<? super InputType>	DefaultKernelContainer.getKernel() Gets the internal kernel.
Kernel<? super InputType>	KernelContainer.getKernel() Gets the kernel.

Methods in gov.sandia.cognition.learning.function.kernel that return types with arguments of type Kernel

Modifier and Type	Method and Description
java.util.Collection<? extends Kernel<? super InputType>>	DefaultKernelsContainer.getKernels() Gets the collection of kernels.

Methods in gov.sandia.cognition.learning.function.kernel with parameters of type Kernel

Modifier and Type	Method and Description
static <ValueType> double	KernelUtil.norm2(ValueType value, Kernel<? super ValueType> kernel) Computes the 2-norm of the given value according to the given kernel.
static <ValueType> double	KernelUtil.norm2Squared(ValueType value, Kernel<? super ValueType> kernel) Computes the squared 2-norm of the given value according to the given kernel.
void	DefaultKernelContainer.setKernel(Kernel<? super InputType> kernel) Sets the internal kernel.

Method parameters in gov.sandia.cognition.learning.function.kernel with type arguments of type Kernel

Modifier and Type	Method and Description
void	DefaultKernelsContainer.setKernels(java.util.Collection<? extends Kernel<? super InputType>> kernels) Sets the collection of kernels.

Constructors in gov.sandia.cognition.learning.function.kernel with parameters of type Kernel

Constructor and Description
DefaultKernelContainer(Kernel<? super InputType> kernel) Creates a new instance of KernelContainer with the given kernel.
ExponentialKernel(Kernel<? super InputType> kernel) Creates a new instance of ExponentialKernel.
KernelDistanceMetric(Kernel<? super InputType> kernel) Creates a new instance of KernelDistanceMetric using the given kernel.
NormalizedKernel(Kernel<? super InputType> kernel) Creates a new instance of NormalizedKernel using the given kernel.
VectorFunctionKernel(VectorFunction function, Kernel<? super Vector> kernel) Creates a new VectorFunctionKernel from the given function and kernel.
WeightedKernel(double weight, Kernel<? super InputType> kernel) Creates a new instance of WeightedKernel from the given weight and kernel.

Constructor parameters in gov.sandia.cognition.learning.function.kernel with type arguments of type Kernel

Constructor and Description
DefaultKernelsContainer(java.util.Collection<? extends Kernel<? super InputType>> kernels) Creates a new instance of DefaultKernelsContainer.
ProductKernel(java.util.Collection<? extends Kernel<? super InputType>> kernels) Creates a new instance of ProductKernel with the given collection of kernels.
SumKernel(java.util.Collection<? extends Kernel<? super InputType>> kernels) Creates a new instance of SumKernel with the given collection of kernels.

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

Constructors in gov.sandia.cognition.learning.function.scalar with parameters of type Kernel

Constructor and Description
KernelScalarFunction(Kernel<? super InputType> kernel) Creates a new instance of KernelScalarFunction with the given kernel.
KernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias) Creates a new instance of KernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel and weighted examples.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias, double constantWeight, double constantValue) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.

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

Constructors in gov.sandia.cognition.statistics.bayesian with parameters of type Kernel

Constructor and Description
GaussianProcessRegression(Kernel<InputType> kernel, double outputVariance) Creates a new instance of GaussianProcessRegression