Uses of Interface
gov.sandia.cognition.learning.algorithm.SupervisedBatchLearner

Packages that use SupervisedBatchLearner

Package	Description
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
gov.sandia.cognition.learning.algorithm.baseline	Provides baseline (dummy) learning algorithms.
gov.sandia.cognition.learning.algorithm.bayes	Provides algorithms for computing Bayesian categorizers.
gov.sandia.cognition.learning.algorithm.confidence	Provides confidence-weighted categorization algorithms.
gov.sandia.cognition.learning.algorithm.delta	Provides an abstract class for helping to implement variants of the Burrows' Delta algorithm.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.factor.machine	Provides factorization machine algorithms.
gov.sandia.cognition.learning.algorithm.nearest	Provides algorithms for Nearest-Neighbor memory-based functions.
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.algorithm.tree	Provides decision tree learning algorithms.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.learning.function.scalar	Provides functions that output real numbers.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm

Subinterfaces of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm

Modifier and Type	Interface and Description
interface	SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> Interface for a class that is a supervised learning algorithm that can be used both batch and incremental contexts.

Classes in gov.sandia.cognition.learning.algorithm that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> The AbstractAnytimeSupervisedBatchLearner abstract class extends the AbstractAnytimeBatchLearner to implement the SupervisedBatchLearner interface.
class	AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> An abstract implementation of the batch and incremental learning for an incremental supervised learner.
class	InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType> An adapter class for performing supervised learning from data where both the input and output have to be transformed before they are passed to the learning algorithm.
class	TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A learner used to predict the future of a sequence of data by wrapping another learner and created a future-aligned data set.

Methods in gov.sandia.cognition.learning.algorithm that return SupervisedBatchLearner

Modifier and Type	Method and Description
SupervisedBatchLearner<InputType,OutputType,EvaluatorType>	TimeSeriesPredictionLearner.getSupervisedLearner() Getter for supervisedLearner

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

Modifier and Type	Method and Description
void	TimeSeriesPredictionLearner.setSupervisedLearner(SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner) Setter for supervisedLearner

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

Constructor and Description
TimeSeriesPredictionLearner(int predictionHorizon, SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner) Creates a new instance of TimeSeriesPredictionLearner

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.baseline

Classes in gov.sandia.cognition.learning.algorithm.baseline that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	MeanLearner The MeanLearner class implements a baseline learner that computes the mean of a given set of values.
class	MostFrequentLearner<OutputType> The MostFrequentLearner class implements a baseline learner that computes the most frequent output value.
class	WeightedMeanLearner The WeightedMeanLearner class implements a baseline learner that computes the weighted mean output value.
class	WeightedMostFrequentLearner<OutputType> The WeightedMostFrequentLearner class implements a baseline learning algorithm that finds the most frequent output of a given dataset based on the weights of the examples.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.bayes

Classes in gov.sandia.cognition.learning.algorithm.bayes that implement SupervisedBatchLearner

Modifier and Type	Class and Description
static class	DiscreteNaiveBayesCategorizer.Learner<InputType,CategoryType> Learner for a DiscreteNaiveBayesCategorizer.
static class	VectorNaiveBayesCategorizer.BatchGaussianLearner<CategoryType> A supervised batch distributionLearner for a vector Naive Bayes categorizer that fits a Gaussian.
static class	VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction> A supervised batch distributionLearner for a vector Naive Bayes categorizer.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.confidence

Classes in gov.sandia.cognition.learning.algorithm.confidence that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AdaptiveRegularizationOfWeights An implementation of the Adaptive Regularization of Weights (AROW) algorithm for online learning of a linear binary categorizer.
class	ConfidenceWeightedDiagonalDeviation An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalDeviationProject An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalVariance An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.
class	ConfidenceWeightedDiagonalVarianceProject An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.delta

Classes in gov.sandia.cognition.learning.algorithm.delta that implement SupervisedBatchLearner

Modifier and Type	Class and Description
static class	AbstractDeltaCategorizer.AbstractLearner<CategoryType> Abstract learner for delta algorithms.
static class	BurrowsDeltaCategorizer.Learner<CategoryType> Learner for a BurrowsDeltaCategorizer.
static class	CosineDeltaCategorizer.Learner<CategoryType> Learner for a CosineDeltaCategorizer.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.ensemble

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>> Learns an ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	AdaBoost<InputType> The AdaBoost class implements the Adaptive Boosting (AdaBoost) algorithm formulated by Yoav Freund and Robert Shapire.
class	BaggingCategorizerLearner<InputType,CategoryType> Learns an categorization ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	BaggingRegressionLearner<InputType> Learns an ensemble for regression by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	BinaryBaggingLearner<InputType> The BinaryBaggingLearner implements the Bagging learning algorithm.
class	BinaryCategorizerSelector<InputType> The BinaryCategorizerSelector class implements a "weak learner" meant for use in boosting algorithms that selects the best BinaryCategorizer from a pre-set list by picking the one with the best weighted error.
class	CategoryBalancedBaggingLearner<InputType,CategoryType> An extension of the basic bagging learner that attempts to sample bags that have equal numbers of examples from every category.
class	CategoryBalancedIVotingLearner<InputType,CategoryType> An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.
class	IVotingCategorizerLearner<InputType,CategoryType> Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets.
class	MultiCategoryAdaBoost<InputType,CategoryType> An implementation of a multi-class version of the Adaptive Boosting (AdaBoost) algorithm, known as AdaBoost.M1.
class	OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.factor.machine

Classes in gov.sandia.cognition.learning.algorithm.factor.machine that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AbstractFactorizationMachineLearner An abstract class for learning FactorizationMachines.
class	FactorizationMachineAlternatingLeastSquares Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.
class	FactorizationMachineStochasticGradient Implements a Stochastic Gradient Descent (SGD) algorithm for learning a Factorization Machine.

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.nearest

Classes in gov.sandia.cognition.learning.algorithm.nearest that implement SupervisedBatchLearner

Modifier and Type	Class and Description
static class	KNearestNeighborExhaustive.Learner<InputType,OutputType> This is a BatchLearner interface for creating a new KNearestNeighborExhaustive from a given dataset, simply a pass-through to the constructor of KNearestNeighborExhaustive
static class	KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> This is a BatchLearner interface for creating a new KNearestNeighbor from a given dataset, simply a pass-through to the constructor of KNearestNeighbor
static class	NearestNeighborExhaustive.Learner<InputType,OutputType> The NearestNeighborExhaustive.Learner class implements a batch learner for the NearestNeighborExhaustive class.
static class	NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor

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

Subinterfaces of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.perceptron

Modifier and Type	Interface and Description
interface	KernelizableBinaryCategorizerOnlineLearner Interface for an online learner of a linear binary categorizer that can also be used with a kernel function.
interface	LinearizableBinaryCategorizerOnlineLearner<InputType> Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer.

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AbstractKernelizableBinaryCategorizerOnlineLearner An abstract implementation of the KernelizableBinaryCategorizerOnlineLearner interface.
class	AbstractLinearCombinationOnlineLearner An abstract class for online learning of linear binary categorizers that take the form of a weighted sum of inputs.
class	AbstractOnlineLinearBinaryCategorizerLearner An abstract class for online (incremental) learning algorithms that produce an LinearBinaryCategorizer.
class	AggressiveRelaxedOnlineMaximumMarginAlgorithm An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm (AROMMA).
class	Ballseptron An implementation of the Ballseptron algorithm.
class	BatchMultiPerceptron<CategoryType> Implements a multi-class version of the standard batch Perceptron learning algorithm.
class	OnlineBinaryMarginInfusedRelaxedAlgorithm An implementation of the binary MIRA algorithm.
class	OnlinePassiveAggressivePerceptron An implementation of the Passive-Aggressive algorithm for learning a linear binary categorizer.
static class	OnlinePassiveAggressivePerceptron.AbstractSoftMargin An abstract class for soft-margin versions of the Passive-Aggressive algorithm.
static class	OnlinePassiveAggressivePerceptron.LinearSoftMargin An implementation of the linear soft-margin variant of the Passive- Aggressive algorithm (PA-I).
static class	OnlinePassiveAggressivePerceptron.QuadraticSoftMargin An implementation of the quadratic soft-margin variant of the Passive- Aggressive algorithm (PA-II).
class	OnlinePerceptron An online version of the classic Perceptron algorithm.
class	OnlineRampPassiveAggressivePerceptron An implementation of the Ramp Loss Passive Aggressive Perceptron (PA^R) from the referenced paper.
class	OnlineShiftingPerceptron An implementation of the Shifting Perceptron algorithm.
class	OnlineVotedPerceptron An online version of the Voted-Perceptron algorithm.
class	Perceptron The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.
class	RelaxedOnlineMaximumMarginAlgorithm An implementation of the Relaxed Online Maximum Margin Algorithm (ROMMA).
class	Winnow An implementation of the Winnow incremental learning algorithm.

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

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

Modifier and Type	Class and Description
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.
class	KernelAdatron<InputType> The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm.
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	KernelPerceptron<InputType> The KernelPerceptron class implements the kernel version of the Perceptron algorithm.
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 SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.regression

Subinterfaces of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.regression

Modifier and Type	Interface and Description
interface	MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>> A regression algorithm that maps one or more independent (input) variables onto multiple output variables.
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
interface	Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A supervised learning algorithm that attempts to interpolate/extrapolate inputs given a training set of input/output pairs.
interface	UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>> A type of Regression algorithm that has a single dependent (output) variable that we are trying to predict.

Classes in gov.sandia.cognition.learning.algorithm.regression that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> Abstract partial implementation for logistic regression classes.
class	AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>> Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
class	AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> Partial implementation of ParameterCostMinimizer.
class	FletcherXuHybridEstimation The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
class	GaussNewtonAlgorithm Implementation of the Gauss-Newton parameter-estimation procedure.
class	KernelBasedIterativeRegression<InputType> The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
class	KernelWeightedRobustRegression<InputType,OutputType> KernelWeightedRobustRegression takes a supervised learning algorithm that operates on a weighted collection of InputOutputPairs and modifies the weight of a sample based on the dataset output and its corresponding estimate from the Evaluator from the supervised learning algorithm at each iteration.
class	LeastSquaresEstimator Abstract implementation of iterative least-squares estimators.
class	LevenbergMarquardtEstimation Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
class	LinearBasisRegression<InputType> Computes the least-squares regression for a LinearCombinationFunction given a dataset.
class	LinearRegression Computes the least-squares regression for a LinearCombinationFunction given a dataset.
static class	LocallyWeightedFunction.Learner<InputType,OutputType> Learning algorithm for creating LocallyWeightedFunctions.
class	LogisticRegression Performs Logistic Regression by means of the iterative reweighted least squares (IRLS) algorithm, where the logistic function has an explicit bias term, and a diagonal L2 regularization term.
class	MultivariateLinearRegression Performs multivariate regression with an explicit bias term, with optional L2 regularization.
class	ParameterDerivativeFreeCostMinimizer Implementation of a class of objects that uses a derivative-free minimization algorithm.
class	ParameterDifferentiableCostMinimizer This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.
class	UnivariateLinearRegression An implementation of simple univariate linear regression.

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

Modifier and Type	Method and Description
SupervisedBatchLearner<InputType,OutputType,?>	KernelWeightedRobustRegression.getIterationLearner() Getter for iterationLearner
SupervisedBatchLearner<InputType,OutputType,?>	LocallyWeightedFunction.getLearner() Getter for learner
SupervisedBatchLearner<InputType,OutputType,?>	LocallyWeightedFunction.Learner.getLearner() Getter for learner

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

Modifier and Type	Method and Description
void	KernelWeightedRobustRegression.setIterationLearner(SupervisedBatchLearner<InputType,OutputType,?> iterationLearner)
void	LocallyWeightedFunction.setLearner(SupervisedBatchLearner<InputType,OutputType,?> learner) Setter for learner
void	LocallyWeightedFunction.Learner.setLearner(SupervisedBatchLearner<InputType,OutputType,?> learner) Setter for learner

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

Constructor and Description
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 SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.svm

Classes in gov.sandia.cognition.learning.algorithm.svm that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	PrimalEstimatedSubGradient An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS) algorithm for learning a linear support vector machine (SVM).
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.
class	SuccessiveOverrelaxation<InputType> The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).

Uses of SupervisedBatchLearner in gov.sandia.cognition.learning.algorithm.tree

Classes in gov.sandia.cognition.learning.algorithm.tree that implement SupervisedBatchLearner

Modifier and Type	Class and Description
class	CategorizationTreeLearner<InputType,OutputType> The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.
class	RegressionTreeLearner<InputType> The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regression learner.

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

Classes in gov.sandia.cognition.learning.function.categorization that implement SupervisedBatchLearner

Modifier and Type	Class and Description
static class	BinaryVersusCategorizer.Learner<InputType,CategoryType> A learner for the BinaryVersusCategorizer.
static class	EvaluatorToCategorizerAdapter.Learner<InputType,CategoryType> The EvaluatorToCategorizerAdapter.Learner class implements a simple supervised learner for a EvaluatorToCategorizerAdapter.
static class	FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver This class implements a closed form solver for the Fisher linear discriminant binary categorizer.
static class	MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType> Learner for the MAP categorizer
static class	WinnerTakeAllCategorizer.Learner<InputType,CategoryType> A learner for the adapter.

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

Classes in gov.sandia.cognition.learning.function.scalar that implement SupervisedBatchLearner

Modifier and Type	Class and Description
static class	PolynomialFunction.Regression Performs Linear Regression using an arbitrary set of PolynomialFunction basis functions
static class	VectorFunctionToScalarFunction.Learner<InputType> The VectorFunctionToScalarFunction.Learner class implements a simple learner for a VectorFunctionToScalarFunction that allows a learning algorithm that outputs a vector function to be adapted to learn on data whose output are doubles.