Uses of Interface

Packages that use DiscriminantCategorizer
gov.sandia.cognition.learning.algorithm.bayes Provides algorithms for computing Bayesian categorizers. 
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 DiscriminantCategorizer in gov.sandia.cognition.learning.algorithm.bayes

Classes in gov.sandia.cognition.learning.algorithm.bayes that implement DiscriminantCategorizer
 class DiscreteNaiveBayesCategorizer<InputType,CategoryType>
          Implementation of a Naive Bayes Classifier for Discrete Data.
 class VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
          A naive Bayesian categorizer that takes an input vector and applies an independent scalar probability density function to each one.

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

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement DiscriminantCategorizer
 class VotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
          An ensemble of categorizers that determine the result based on an equal-weight vote.
 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.
 class WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
          An ensemble of categorizers where each ensemble member is evaluated with the given input to find the category to which its weighted votes are assigned.

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

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

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

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement DiscriminantCategorizer
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 DiscriminantCategorizer in gov.sandia.cognition.learning.function.categorization

Subinterfaces of DiscriminantCategorizer 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 DiscriminantCategorizer
 class AbstractConfidenceWeightedBinaryCategorizer
          Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
 class AbstractDiscriminantBinaryCategorizer<InputType>
          An abstract implementation of the DiscriminantBinaryCategorizer interface.
 class AbstractDiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>>
          An abstract implementation of the DiscriminantCategorizer 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 BinaryVersusCategorizer<InputType,CategoryType>
          An adapter that allows binary categorizers to be adapted for multi-category problems by applying a binary categorizer to each pair of categories.
 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 LinearMultiCategorizer<CategoryType>
          A multi-category version of the LinearBinaryCategorizer that keeps a separate LinearBinaryCategorizer for each category.
 class MaximumAPosterioriCategorizer<ObservationType,CategoryType>
          Categorizer that returns the category with the highest posterior likelihood for a given observation.
 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.
 class WinnerTakeAllCategorizer<InputType,CategoryType>
          Adapts an evaluator that outputs a vector to be used as a categorizer.