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See:
Description
Interface Summary | |
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ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> | A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object. |
Class Summary | |
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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. |
Provides regression algorithms, such as Linear Regression.
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