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

Packages that use gov.sandia.cognition.learning.algorithm.regression
gov.sandia.cognition.learning.algorithm.regression Provides regression algorithms, such as Linear Regression. 
 

Classes in gov.sandia.cognition.learning.algorithm.regression used by gov.sandia.cognition.learning.algorithm.regression
AbstractMinimizerBasedParameterCostMinimizer
          Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
AbstractParameterCostMinimizer
          Partial implementation of ParameterCostMinimizer.
KernelBasedIterativeRegression
          The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
LeastSquaresEstimator
          Abstract implementation of iterative least-squares estimators.
LinearBasisRegression
          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
          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".
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.
ParameterCostMinimizer
          A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
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.