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 multiclass version of the Adaptive Boosting
(AdaBoost) algorithm, known as AdaBoost.M1.

Modifier and Type  Class and Description 

class 
AbstractFactorizationMachineLearner
An abstract class for learning
FactorizationMachine s. 
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 multiclass 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 FletcherXu hybrid estimation for solving the nonlinear leastsquares
parameters.

class 
GaussNewtonAlgorithm
Implementation of the GaussNewton parameterestimation 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 leastsquares estimators.

class 
LevenbergMarquardtEstimation
Implementation of the nonlinear regression algorithm, known as
LevenbergMarquardt 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 SubGradient 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 kernelbased binary
categorizer.

class 
SuccessiveOverrelaxation<InputType>
The
SuccessiveOverrelaxation class implements the Successive
Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM). 