Uses of Class
gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer

Packages that use LinearBinaryCategorizer

Package	Description
gov.sandia.cognition.learning.algorithm.perceptron	Provides the Perceptron algorithm and some of its variations.
gov.sandia.cognition.learning.algorithm.svm	Provides implementations of Support Vector Machine (SVM) learning algorithms.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.

Uses of LinearBinaryCategorizer in gov.sandia.cognition.learning.algorithm.perceptron

Subclasses of LinearBinaryCategorizer in gov.sandia.cognition.learning.algorithm.perceptron

Modifier and Type	Class and Description
static class	OnlineShiftingPerceptron.LinearResult This is the result learned by the shifting perceptron.

Methods in gov.sandia.cognition.learning.algorithm.perceptron that return LinearBinaryCategorizer

Modifier and Type	Method and Description
LinearBinaryCategorizer	AbstractOnlineLinearBinaryCategorizerLearner.createInitialLearnedObject()
LinearBinaryCategorizer	OnlineShiftingPerceptron.createInitialLearnedObject()
LinearBinaryCategorizer	Winnow.createInitialLearnedObject()
LinearBinaryCategorizer	LinearizableBinaryCategorizerOnlineLearner.createInitialLinearLearnedObject(VectorFactory<?> vectorFactory) Creates the initial learned object.
LinearBinaryCategorizer	Perceptron.getResult()

Methods in gov.sandia.cognition.learning.algorithm.perceptron that return types with arguments of type LinearBinaryCategorizer

Modifier and Type	Method and Description
WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer>	OnlineVotedPerceptron.createInitialLearnedObject()
SupervisedIncrementalLearner<Vectorizable,java.lang.Boolean,LinearBinaryCategorizer>	LinearizableBinaryCategorizerOnlineLearner.createLinearLearner(VectorFactory<?> vectorFactory) Creates a new linear learner using the standard learning interfaces based on this learner and its parameters.
static DefaultWeightedValue<LinearBinaryCategorizer>	OnlineVotedPerceptron.getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble) Gets the last member in the ensemble.

Methods in gov.sandia.cognition.learning.algorithm.perceptron with parameters of type LinearBinaryCategorizer

Modifier and Type	Method and Description
protected double	AbstractLinearCombinationOnlineLearner.computeDecay(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted, double update) Computes the decay scalar for the existing weight vector.
protected double	OnlineShiftingPerceptron.computeDecay(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted, double update)
protected double	AbstractLinearCombinationOnlineLearner.computeRescaling(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted, double update, double decay) Computes the rescaling for the new weight vector.
protected abstract double	AbstractLinearCombinationOnlineLearner.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted) Compute the update weight in the linear case.
protected double	OnlineBinaryMarginInfusedRelaxedAlgorithm.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted)
double	OnlinePassiveAggressivePerceptron.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted)
double	OnlinePerceptron.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean label, double predicted)
double	OnlineRampPassiveAggressivePerceptron.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean actualCategory, double predicted)
double	OnlineShiftingPerceptron.computeUpdate(LinearBinaryCategorizer target, Vector input, boolean label, double predicted)
protected void	AbstractLinearCombinationOnlineLearner.initialize(LinearBinaryCategorizer target, Vector input, boolean actualCategory) Initializes the linear binary categorizer.
protected void	OnlineBinaryMarginInfusedRelaxedAlgorithm.initialize(LinearBinaryCategorizer target, Vector input, boolean actualCategory)
protected void	Perceptron.setResult(LinearBinaryCategorizer result) Sets the object currently being result.
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, InputOutputPair<? extends Vectorizable,java.lang.Boolean> data, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>> data, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.
void	AbstractLinearCombinationOnlineLearner.update(LinearBinaryCategorizer target, Vector input, boolean label)
abstract void	AbstractOnlineLinearBinaryCategorizerLearner.update(LinearBinaryCategorizer target, Vector input, boolean output) The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.
void	AggressiveRelaxedOnlineMaximumMarginAlgorithm.update(LinearBinaryCategorizer target, Vector input, boolean label)
void	Ballseptron.update(LinearBinaryCategorizer target, Vector input, boolean label)
void	RelaxedOnlineMaximumMarginAlgorithm.update(LinearBinaryCategorizer target, Vector input, boolean label)
void	Winnow.update(LinearBinaryCategorizer target, Vector input, boolean actual)
void	AbstractOnlineLinearBinaryCategorizerLearner.update(LinearBinaryCategorizer target, Vectorizable input, java.lang.Boolean output)
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, Vectorizable input, boolean output, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, Vectorizable input, java.lang.Boolean output, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.

Method parameters in gov.sandia.cognition.learning.algorithm.perceptron with type arguments of type LinearBinaryCategorizer

Modifier and Type	Method and Description
static DefaultWeightedValue<LinearBinaryCategorizer>	OnlineVotedPerceptron.getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble) Gets the last member in the ensemble.
void	OnlineVotedPerceptron.update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target, Vector input, boolean actual) The update method updates an object of ResultType using the given a new supervised input-output pair, using some form of "learning" algorithm.
void	OnlineVotedPerceptron.update(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> target, Vectorizable input, java.lang.Boolean output)

Uses of LinearBinaryCategorizer in gov.sandia.cognition.learning.algorithm.svm

Fields in gov.sandia.cognition.learning.algorithm.svm declared as LinearBinaryCategorizer

Modifier and Type	Field and Description
protected LinearBinaryCategorizer	PrimalEstimatedSubGradient.result The categorizer learned as a result of the algorithm.

Methods in gov.sandia.cognition.learning.algorithm.svm that return LinearBinaryCategorizer

Modifier and Type	Method and Description
LinearBinaryCategorizer	PrimalEstimatedSubGradient.getResult()

Uses of LinearBinaryCategorizer in gov.sandia.cognition.learning.function.categorization

Subclasses of LinearBinaryCategorizer in gov.sandia.cognition.learning.function.categorization

Modifier and Type	Class and Description
class	AbstractConfidenceWeightedBinaryCategorizer Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
class	DefaultConfidenceWeightedBinaryCategorizer A default implementation of the ConfidenceWeightedBinaryCategorizer that stores a full mean and covariance matrix.
class	DiagonalConfidenceWeightedBinaryCategorizer A confidence-weighted linear predictor with a diagonal covariance, which is stored as a vector.

Fields in gov.sandia.cognition.learning.function.categorization with type parameters of type LinearBinaryCategorizer

Modifier and Type	Field and Description
protected java.util.Map<CategoryType,LinearBinaryCategorizer>	LinearMultiCategorizer.prototypes A map of each category to its prototype categorizer.

Methods in gov.sandia.cognition.learning.function.categorization that return LinearBinaryCategorizer

Modifier and Type	Method and Description
LinearBinaryCategorizer	LinearBinaryCategorizer.clone()

Methods in gov.sandia.cognition.learning.function.categorization that return types with arguments of type LinearBinaryCategorizer

Modifier and Type	Method and Description
java.util.Map<CategoryType,LinearBinaryCategorizer>	LinearMultiCategorizer.getPrototypes() Gets the mapping of categories to prototypes.

Method parameters in gov.sandia.cognition.learning.function.categorization with type arguments of type LinearBinaryCategorizer

Modifier and Type	Method and Description
void	LinearMultiCategorizer.setPrototypes(java.util.Map<CategoryType,LinearBinaryCategorizer> prototypes) Sets the mapping of categories to prototypes.

Constructors in gov.sandia.cognition.learning.function.categorization with parameters of type LinearBinaryCategorizer

Constructor and Description
LinearBinaryCategorizer(LinearBinaryCategorizer other) Creates a new copy of a LinearBinaryCategorizer.

Constructor parameters in gov.sandia.cognition.learning.function.categorization with type arguments of type LinearBinaryCategorizer

Constructor and Description
LinearMultiCategorizer(java.util.Map<CategoryType,LinearBinaryCategorizer> prototypes) Creates a new LinearMultiCategorizer with the given prototypes.