| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| Class and Description |
|---|
| AbstractLogisticRegression
Abstract partial implementation for logistic regression classes.
|
| 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.
|
| Regression
A supervised learning algorithm that attempts to interpolate/extrapolate
inputs given a training set of input/output pairs.
|