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

Packages that use ParameterCostMinimizer
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
 class AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends 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.