See: Description
| Interface | Description |
|---|---|
| MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>> |
A regression algorithm that maps one or more independent (input) variables
onto multiple output variables.
|
| ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> |
A anytime algorithm that is used to estimate the locally minimum-cost
parameters of an object.
|
| Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> |
A supervised learning algorithm that attempts to interpolate/extrapolate
inputs given a training set of input/output pairs.
|
| UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>> |
A type of Regression algorithm that has a single dependent (output) variable
that we are trying to predict.
|
| Class | Description |
|---|---|
| AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> |
Abstract partial implementation for logistic regression classes.
|
| 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.
|
| 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.
|