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

Packages that use BatchCostMinimizationLearner

Package	Description
gov.sandia.cognition.learning.algorithm.annealing	Provides the Simulated Annealing algorithm.
gov.sandia.cognition.learning.algorithm.genetic	Provides a genetic algorithm implementation.
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.

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

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement BatchCostMinimizationLearner

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

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement BatchCostMinimizationLearner

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 BatchCostMinimizationLearner in gov.sandia.cognition.learning.algorithm.regression

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

Modifier and Type	Interface and Description
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.

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

Modifier and Type	Class and Description
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	LeastSquaresEstimator Abstract implementation of iterative least-squares estimators.
class	LevenbergMarquardtEstimation Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
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.