Uses of Class
gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

Packages that use AbstractAnytimeBatchLearner

Package	Description
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
gov.sandia.cognition.learning.algorithm.annealing	Provides the Simulated Annealing algorithm.
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
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.genetic	Provides a genetic algorithm implementation.
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.algorithm.minimization	Provides minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.line	Provides line (scalar) minimization algorithms.
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.root	Provides algorithms for finding the roots, or zero crossings, of scalar functions.
gov.sandia.cognition.learning.algorithm.svm	Provides implementations of Support Vector Machine (SVM) learning algorithms.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.text.topic	Provides topic modeling algorithms.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm

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.

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

Modifier and Type	Method and Description
AbstractAnytimeBatchLearner<DataType,ResultType>	AbstractAnytimeBatchLearner.clone()

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.annealing

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.annealing

Modifier and Type	Class and Description
class	SimulatedAnnealer<CostParametersType,AnnealedType> The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.clustering

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.clustering

Modifier and Type	Class and Description
class	AffinityPropagation<DataType> The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
class	AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
class	DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> The DBSCAN algorithm requires three parameters: a distance metric, a value for neighborhood radius, and a value for the minimum number of surrounding neighbors for a point to be considered non-noise.
class	KMeansClusterer<DataType,ClusterType extends Cluster<DataType>> The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
class	KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>> Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
class	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
class	OptimizedKMeansClusterer<DataType> This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
class	ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>> This is a parallel implementation of the k-means clustering algorithm.
class	PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> The PartitionalClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.ensemble

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	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.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.factor.machine

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 AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.genetic

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.genetic

Modifier and Type	Class and Description
class	GeneticAlgorithm<CostParametersType,GenomeType> The GeneticAlgorithm class implements a generic genetic algorithm that uses a given cost function to minimize and a given reproduction function for generating the population.
class	ParallelizedGeneticAlgorithm<CostParametersType,GenomeType> This is a parallel implementation of the genetic algorithm.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.hmm

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.hmm

Modifier and Type	Class and Description
class	AbstractBaumWelchAlgorithm<ObservationType,DataType> Partial implementation of the Baum-Welch algorithm.
class	BaumWelchAlgorithm<ObservationType> Implements the Baum-Welch algorithm, also known as the "forward-backward algorithm", the expectation-maximization algorithm, etc for Hidden Markov Models (HMMs).
class	ParallelBaumWelchAlgorithm<ObservationType> A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.minimization

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.minimization

Modifier and Type	Class and Description
class	AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
class	FunctionMinimizerBFGS Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.
class	FunctionMinimizerConjugateGradient Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.
class	FunctionMinimizerDFP Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.
class	FunctionMinimizerDirectionSetPowell Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.
class	FunctionMinimizerFletcherReeves This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.
class	FunctionMinimizerGradientDescent This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.
class	FunctionMinimizerLiuStorey This is an implementation of the Liu-Storey conjugate gradient minimization procedure.
class	FunctionMinimizerNelderMead Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.
class	FunctionMinimizerPolakRibiere This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.
class	FunctionMinimizerQuasiNewton This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.minimization.line

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.minimization.line

Modifier and Type	Class and Description
class	AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Partial AnytimeAlgorithm implementation of a LineMinimizer.
class	LineMinimizerBacktracking Implementation of the backtracking line-minimization algorithm.
class	LineMinimizerDerivativeBased This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
class	LineMinimizerDerivativeFree This is an implementation of a LineMinimizer that does not require derivative information.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.pca

Modifier and Type	Class and Description
class	GeneralizedHebbianAlgorithm Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.perceptron

Modifier and Type	Class and Description
class	BatchMultiPerceptron<CategoryType> Implements a multi-class version of the standard batch Perceptron learning algorithm.
class	Perceptron The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.perceptron.kernel

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

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.regression

Modifier and Type	Class and Description
class	AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> Abstract partial implementation for logistic regression classes.
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	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.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.root

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.root

Modifier and Type	Class and Description
class	AbstractBracketedRootFinder Partial implementation of RootFinder that maintains a bracket on the root.
class	AbstractRootFinder Partial implementation of RootFinder.
class	RootBracketExpander The root-bracketing expansion algorithm.
class	RootFinderBisectionMethod Bisection algorithm for root finding.
class	RootFinderFalsePositionMethod The false-position algorithm for root finding.
class	RootFinderNewtonsMethod Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
class	RootFinderRiddersMethod The root-finding algorithm due to Ridders.
class	RootFinderSecantMethod The secant algorithm for root finding.

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

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.learning.algorithm.svm

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 AbstractAnytimeBatchLearner in gov.sandia.cognition.statistics.bayesian

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.statistics.bayesian

Modifier and Type	Class and Description
class	AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> Partial abstract implementation of MarkovChainMonteCarlo.
class	DirichletProcessMixtureModel<ObservationType> An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
class	MetropolisHastingsAlgorithm<ObservationType,ParameterType> An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
class	ParallelDirichletProcessMixtureModel<ObservationType> A Parallelized version of vanilla Dirichlet Process Mixture Model learning.

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.statistics.distribution

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.statistics.distribution

Modifier and Type	Class and Description
static class	MixtureOfGaussians.EMLearner An Expectation-Maximization based "soft" assignment learner.
static class	ScalarMixtureDensityModel.EMLearner An EM learner that estimates a mixture model from data

Uses of AbstractAnytimeBatchLearner in gov.sandia.cognition.text.topic

Subclasses of AbstractAnytimeBatchLearner in gov.sandia.cognition.text.topic

Modifier and Type	Class and Description
class	LatentDirichletAllocationVectorGibbsSampler A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
class	ParallelLatentDirichletAllocationVectorGibbsSampler A parallel implementation of LatentDirichletAllocationVectorGibbsSampler.
class	ProbabilisticLatentSemanticAnalysis An implementation of the Probabilistic Latent Semantic Analysis (PLSA) algorithm.