Uses of Interface

Packages that use BinaryCategorizer
gov.sandia.cognition.learning.algorithm.ensemble Provides ensmble methods. 
gov.sandia.cognition.learning.algorithm.perceptron Provides the Perceptron algorithm and some of its variations. 
gov.sandia.cognition.learning.function.categorization Provides functions that output a discrete set of categories. 

Uses of BinaryCategorizer in gov.sandia.cognition.learning.algorithm.ensemble

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement BinaryCategorizer
 class WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Boolean>>
          The WeightedBinaryEnsemble class implements an Ensemble of BinaryCategorizer objects where each categorizer is assigned a weight and the category is selected by choosing the one with the largest sum of weights.

Fields in gov.sandia.cognition.learning.algorithm.ensemble with type parameters of type BinaryCategorizer
protected  Collection<BinaryCategorizer<? super InputType>> BinaryCategorizerSelector.categorizers
          The collection of categorizers to evaluate and select from.

Methods in gov.sandia.cognition.learning.algorithm.ensemble that return BinaryCategorizer
 BinaryCategorizer<? super InputType> BinaryCategorizerSelector.learn(Collection<? extends InputOutputPair<? extends InputType,Boolean>> data)
          Selects the BinaryCategorizer from its list of categorizers that minimizes the weighted error on the given set of weighted input-output pairs.

Methods in gov.sandia.cognition.learning.algorithm.ensemble that return types with arguments of type BinaryCategorizer
 Collection<BinaryCategorizer<? super InputType>> BinaryCategorizerSelector.getCategorizers()
          Gets the collection of categorizers that the learner selects from.

Method parameters in gov.sandia.cognition.learning.algorithm.ensemble with type arguments of type BinaryCategorizer
 void BinaryCategorizerSelector.setCategorizers(Collection<BinaryCategorizer<? super InputType>> categorizers)
          Gets the collection of categorizers that the learner selects from.

Constructor parameters in gov.sandia.cognition.learning.algorithm.ensemble with type arguments of type BinaryCategorizer
BinaryCategorizerSelector(Collection<BinaryCategorizer<? super InputType>> categorizers)
          Creates a new instance of BinaryCategorizerSelector.

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

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement BinaryCategorizer
static class OnlineShiftingPerceptron.LinearResult
          This is the result learned by the shifting perceptron.

Uses of BinaryCategorizer in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement BinaryCategorizer
static class Forgetron.Result<InputType>
          The result object learned by the Forgetron, which extends the DefaultKernelBinaryCategorizer with some additional state information needed in the update step.

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

Subinterfaces of BinaryCategorizer in gov.sandia.cognition.learning.function.categorization
 interface ConfidenceWeightedBinaryCategorizer
          Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers.
 interface DiscriminantBinaryCategorizer<InputType>
          Interface for a linear discriminant categorizer in the binary categorization domain.
 interface ThresholdBinaryCategorizer<InputType>
          Interface for a binary categorizer that uses a threshold to determine the categorization.

Classes in gov.sandia.cognition.learning.function.categorization that implement BinaryCategorizer
 class AbstractBinaryCategorizer<InputType>
          The AbstractBinaryCategorizer implements the commonality of the BinaryCategorizer, holding the collection of possible values.
 class AbstractConfidenceWeightedBinaryCategorizer
          Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
 class AbstractDiscriminantBinaryCategorizer<InputType>
          An abstract implementation of the DiscriminantBinaryCategorizer interface.
 class AbstractThresholdBinaryCategorizer<InputType>
          Categorizer that first maps the input space onto a real value, then uses a threshold to map the result onto lowValue (for strictly less than the threshold) or highValue (for greater than or equal to the threshold).
 class DefaultConfidenceWeightedBinaryCategorizer
          A default implementation of the ConfidenceWeightedBinaryCategorizer that stores a full mean and covariance matrix.
 class DefaultKernelBinaryCategorizer<InputType>
          A default implementation of the KernelBinaryCategorizer that uses the standard way of representing the examples (supports) using a DefaultWeightedValue.
 class DiagonalConfidenceWeightedBinaryCategorizer
          A confidence-weighted linear predictor with a diagonal covariance, which is stored as a vector.
 class FisherLinearDiscriminantBinaryCategorizer
          A Fisher Linear Discriminant classifier, which creates an optimal linear separating plane between two Gaussian classes of different covariances.
 class KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>>
          The KernelBinaryCategorizer class implements a binary categorizer that uses a kernel to do its categorization.
 class LinearBinaryCategorizer
          The LinearBinaryCategorizer class implements a binary categorizer that is implemented by a linear function.
 class ScalarFunctionToBinaryCategorizerAdapter<InputType>
          Adapts a scalar function to be a categorizer using a threshold.
 class ScalarThresholdBinaryCategorizer
          The ScalarThresholdBinaryCategorizer class implements a binary categorizer that uses a threshold to categorize a given double.
 class VectorElementThresholdCategorizer
          The VectorElementThresholdCategorizer class implements a BinaryCategorizer that categorizes an input vector by applying a threshold to an element in a the vector.