Package gov.sandia.cognition.learning.algorithm.regression

Provides regression algorithms, such as Linear Regression.

See:
          Description

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

Class Summary
AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends Double>> Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> Partial implementation of ParameterCostMinimizer.
FletcherXuHybridEstimation The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
GaussNewtonAlgorithm Implementation of the Gauss-Newton parameter-estimation procedure.
KernelBasedIterativeRegression<InputType> The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
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.
LeastSquaresEstimator Abstract implementation of iterative least-squares estimators.
LevenbergMarquardtEstimation Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
LinearBasisRegression<InputType> Computes the least-squares regression for a LinearCombinationFunction given a dataset.
LinearRegression Computes the least-squares regression for a LinearCombinationFunction given a dataset.
LinearRegression.Statistic Computes regression statistics using a chi-square measure of the statistical significance of the learned approximator
LocallyWeightedFunction<InputType,OutputType> LocallyWeightedFunction is a generalization of the k-nearest neighbor concept, also known as "Instance-Based Learning", "Memory-Based Learning", "Nonparametric Regression", "Case-Based Regression", or "Kernel-Based Regression".
LocallyWeightedFunction.Learner<InputType,OutputType> Learning algorithm for creating LocallyWeightedFunctions.
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.
LogisticRegression.Function Class that is a linear discriminant, followed by a sigmoid function.
MultivariateLinearRegression Performs multivariate regression with an explicit bias term, with optional L2 regularization.
ParameterDerivativeFreeCostMinimizer Implementation of a class of objects that uses a derivative-free minimization algorithm.
ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree Function that maps the parameters of an object to its inputs, so that minimization algorithms can tune the parameters of an object against a cost function.
ParameterDifferentiableCostMinimizer This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.
ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased Function that maps the parameters of an object to its inputs, so that minimization algorithms can tune the parameters of an object against a cost function.
UnivariateLinearRegression An implementation of simple univariate linear regression.
 

Package gov.sandia.cognition.learning.algorithm.regression Description

Provides regression algorithms, such as Linear Regression.

Since:
2.0
Author:
Justin Basilico