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

Packages that use LinearBinaryCategorizer
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
static class OnlineShiftingPerceptron.LinearResult
          This is the result learned by the shifting perceptron.
 

Methods in gov.sandia.cognition.learning.algorithm.perceptron that return LinearBinaryCategorizer
 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
 WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> OnlineVotedPerceptron.createInitialLearnedObject()
           
 SupervisedIncrementalLearner<Vectorizable,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
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,Boolean> data, VectorFactory<?> vectorFactory)
          Performs a linear incremental update step on the given object using the given supervised data.
 void LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, Iterable<? extends InputOutputPair<? extends Vectorizable,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, 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, 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
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, Boolean output)
           
 

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

Fields in gov.sandia.cognition.learning.algorithm.svm declared as LinearBinaryCategorizer
protected  LinearBinaryCategorizer PrimalEstimatedSubGradient.result
          The categorizer learned as a result of the algorithm.
 

Methods in gov.sandia.cognition.learning.algorithm.svm that return LinearBinaryCategorizer
 LinearBinaryCategorizer PrimalEstimatedSubGradient.getResult()
           
 

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

Subclasses of LinearBinaryCategorizer in gov.sandia.cognition.learning.function.categorization
 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
protected  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
 LinearBinaryCategorizer LinearBinaryCategorizer.clone()
           
 

Methods in gov.sandia.cognition.learning.function.categorization that return types with arguments of type LinearBinaryCategorizer
 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
 void LinearMultiCategorizer.setPrototypes(Map<CategoryType,LinearBinaryCategorizer> prototypes)
          Sets the mapping of categories to prototypes.
 

Constructors in gov.sandia.cognition.learning.function.categorization with parameters of type LinearBinaryCategorizer
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
LinearMultiCategorizer(Map<CategoryType,LinearBinaryCategorizer> prototypes)
          Creates a new LinearMultiCategorizer with the given prototypes.