See: Description
| Interface | Description |
|---|---|
| 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 java.lang.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 | Description |
|---|---|
| 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 java.lang.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.
|