| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.ensemble |
Provides ensemble methods.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| gov.sandia.cognition.learning.algorithm.perceptron.kernel | |
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.svm |
Provides implementations of Support Vector Machine (SVM) learning algorithms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>>
Learns an ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
AdaBoost<InputType>
The
AdaBoost class implements the Adaptive Boosting (AdaBoost)
algorithm formulated by Yoav Freund and Robert Shapire. |
class |
BaggingCategorizerLearner<InputType,CategoryType>
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
BaggingRegressionLearner<InputType>
Learns an ensemble for regression by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
BinaryBaggingLearner<InputType>
The
BinaryBaggingLearner implements the Bagging learning algorithm. |
class |
CategoryBalancedBaggingLearner<InputType,CategoryType>
An extension of the basic bagging learner that attempts to sample bags that
have equal numbers of examples from every category.
|
class |
CategoryBalancedIVotingLearner<InputType,CategoryType>
An extension of IVoting for dealing with skew problems that makes sure that
there are an equal number of examples from each category in each sample that
an ensemble member is trained on.
|
class |
IVotingCategorizerLearner<InputType,CategoryType>
Learns an ensemble in a method similar to bagging except that on each
iteration the bag is built from two parts, each sampled from elements from
disjoint sets.
|
class |
MultiCategoryAdaBoost<InputType,CategoryType>
An implementation of a multi-class version of the Adaptive Boosting
(AdaBoost) algorithm, known as AdaBoost.M1.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFactorizationMachineLearner
An abstract class for learning
FactorizationMachines. |
class |
FactorizationMachineAlternatingLeastSquares
Implements an Alternating Least Squares (ALS) algorithm for learning a
Factorization Machine.
|
class |
FactorizationMachineStochasticGradient
Implements a Stochastic Gradient Descent (SGD) algorithm for learning a
Factorization Machine.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BatchMultiPerceptron<CategoryType>
Implements a multi-class version of the standard batch Perceptron learning
algorithm.
|
class |
Perceptron
The
Perceptron class implements the standard Perceptron learning
algorithm that learns a binary classifier based on vector input. |
| Modifier and Type | Class and Description |
|---|---|
class |
KernelAdatron<InputType>
The
KernelAdatron class implements an online version of the Support
Vector Machine learning algorithm. |
class |
KernelPerceptron<InputType>
The
KernelPerceptron class implements the kernel version of
the Perceptron algorithm. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>>
Abstract partial implementation for logistic regression classes.
|
class |
AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
Partial implementation of ParameterCostMinimizer.
|
class |
FletcherXuHybridEstimation
The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares
parameters.
|
class |
GaussNewtonAlgorithm
Implementation of the Gauss-Newton parameter-estimation procedure.
|
class |
KernelBasedIterativeRegression<InputType>
The
KernelBasedIterativeRegression class implements an online version of
the Support Vector Regression algorithm. |
class |
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.
|
class |
LeastSquaresEstimator
Abstract implementation of iterative least-squares estimators.
|
class |
LevenbergMarquardtEstimation
Implementation of the nonlinear regression algorithm, known as
Levenberg-Marquardt Estimation (or LMA).
|
class |
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
PrimalEstimatedSubGradient
An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS)
algorithm for learning a linear support vector machine (SVM).
|
class |
SequentialMinimalOptimization<InputType>
An implementation of the Sequential Minimal Optimization (SMO) algorithm for
training a Support Vector Machine (SVM), which is a kernel-based binary
categorizer.
|
class |
SuccessiveOverrelaxation<InputType>
The
SuccessiveOverrelaxation class implements the Successive
Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM). |