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See:
Description
Interface Summary | |
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BinaryCategorizer<InputType> | The BinaryCategorizer extends the Categorizer
interface by enforcing that the output categories are boolean values, which
means that the categorizer is performing binary categorization. |
Categorizer<InputType,CategoryType> | The Categorizer interface defines the functionality of an
object that can take an input and evaluate what category out of a fixed
set of categories it belongs to. |
ConfidenceWeightedBinaryCategorizer | Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers. |
DiscriminantBinaryCategorizer<InputType> | Interface for a linear discriminant categorizer in the binary categorization domain. |
DiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>> | Interface for a Categorizer that can produce a value to discriminate
between how well different instances fit a given category. |
ThresholdBinaryCategorizer<InputType> | Interface for a binary categorizer that uses a threshold to determine the categorization. |
Class Summary | |
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AbstractBinaryCategorizer<InputType> | The AbstractBinaryCategorizer implements the commonality of
the BinaryCategorizer , holding the collection of possible
values. |
AbstractCategorizer<InputType,CategoryType> | An abstract implementation of the Categorizer interface. |
AbstractConfidenceWeightedBinaryCategorizer | Unit tests for class AbstractConfidenceWeightedBinaryCategorizer. |
AbstractDiscriminantBinaryCategorizer<InputType> | An abstract implementation of the DiscriminantBinaryCategorizer
interface. |
AbstractDiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>> | An abstract implementation of the DiscriminantCategorizer interface. |
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). |
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. |
BinaryVersusCategorizer.Learner<InputType,CategoryType> | A learner for the BinaryVersusCategorizer . |
CompositeCategorizer<InputType,IntermediateType,CategoryType> | Composes a preprocessor function with a categorizer. |
DefaultConfidenceWeightedBinaryCategorizer | A default implementation of the ConfidenceWeightedBinaryCategorizer
that stores a full mean and covariance matrix. |
DefaultKernelBinaryCategorizer<InputType> | A default implementation of the KernelBinaryCategorizer that uses
the standard way of representing the examples (supports) using a
DefaultWeightedValue . |
DiagonalConfidenceWeightedBinaryCategorizer | A confidence-weighted linear predictor with a diagonal covariance, which is stored as a vector. |
EvaluatorToCategorizerAdapter<InputType,CategoryType> | The EvaluatorToCategorizerAdapter class implements an adapter from a
general Evaluator to be a Categorizer . |
EvaluatorToCategorizerAdapter.Learner<InputType,CategoryType> | The EvaluatorToCategorizerAdapter.Learner class implements a
simple supervised learner for a EvaluatorToCategorizerAdapter . |
FisherLinearDiscriminantBinaryCategorizer | A Fisher Linear Discriminant classifier, which creates an optimal linear separating plane between two Gaussian classes of different covariances. |
FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver | This class implements a closed form solver for the Fisher linear discriminant binary categorizer. |
KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>> | The KernelBinaryCategorizer class implements a binary
categorizer that uses a kernel to do its categorization. |
LinearBinaryCategorizer | The LinearBinaryCategorizer class implements a binary
categorizer that is implemented by a linear function. |
LinearMultiCategorizer<CategoryType> | A multi-category version of the LinearBinaryCategorizer that keeps a separate LinearBinaryCategorizer for each category. |
MaximumAPosterioriCategorizer<ObservationType,CategoryType> | Categorizer that returns the category with the highest posterior likelihood for a given observation. |
MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType> | Learner for the MAP categorizer |
ScalarFunctionToBinaryCategorizerAdapter<InputType> | Adapts a scalar function to be a categorizer using a threshold. |
ScalarThresholdBinaryCategorizer | The ScalarThresholdBinaryCategorizer class implements a binary
categorizer that uses a threshold to categorize a given double. |
VectorElementThresholdCategorizer | The VectorElementThresholdCategorizer class implements a
BinaryCategorizer that categorizes an input vector by applying a
threshold to an element in a the vector. |
WinnerTakeAllCategorizer<InputType,CategoryType> | Adapts an evaluator that outputs a vector to be used as a categorizer. |
WinnerTakeAllCategorizer.Learner<InputType,CategoryType> | A learner for the adapter. |
Provides functions that output a discrete set of categories.
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