Uses of Interface
gov.sandia.cognition.learning.algorithm.regression.ParameterCostMinimizer

Packages that use ParameterCostMinimizer

Package	Description
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.

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

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

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.