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## Uses of Class
de.jstacs.classifiers.AbstractClassifier

Packages that use AbstractClassifier
de.jstacs.classifiers | This package provides the framework for any classifier.
de.jstacs.classifiers.assessment | This package allows to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased | Provides the classes for `Classifier`s that are based on `SequenceScore`s.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s by a unified generative-discriminative learning principle
de.jstacs.classifiers.differentiableSequenceScoreBased.msp | Provides an implementation of a classifier that allows to train the parameters of a set of `DifferentiableStatisticalModel`s either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling | Provides the classes for `AbstractScoreBasedClassifier`s that are based on `SamplingDifferentiableStatisticalModel`s and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.trainSMBased | Provides the classes for `Classifier`s that are based on `TrainableStatisticalModel`s
de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared |

Uses of AbstractClassifier in de.jstacs.classifiers

Subclasses of AbstractClassifier in de.jstacs.classifiers
`class` | `AbstractScoreBasedClassifier` This class is the main class for all score based classifiers.
`class` | `MappingClassifier` This class allows the user to train the classifier on a given number of classes and to evaluate the classifier on a smaller number of classes by mapping classes together.

Methods in de.jstacs.classifiers that return AbstractClassifier
`AbstractClassifier` | `AbstractClassifier.clone()`

Uses of AbstractClassifier in de.jstacs.classifiers.assessment

Fields in de.jstacs.classifiers.assessment declared as AbstractClassifier
`protected AbstractClassifier[]` | `ClassifierAssessment.myAbstractClassifier` This array contains the internal used classifiers.

Methods in de.jstacs.classifiers.assessment that return AbstractClassifier
`AbstractClassifier[]` | `ClassifierAssessment.getClassifier()` Returns a deep copy of all classifiers that have been or will be used in this assessment.

Constructors in de.jstacs.classifiers.assessment with parameters of type AbstractClassifier
`ClassifierAssessment(AbstractClassifier... aCs)` Creates a new `ClassifierAssessment` from a set of `AbstractClassifier`s.
`ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and, in addition, classifiers that will be constructed using the given `TrainableStatisticalModel`s.
`ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `ClassifierAssessment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`KFoldCrossValidation(AbstractClassifier... aCs)` Creates a new `KFoldCrossValidation` from a set of `AbstractClassifier`s.
`KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `KFoldCrossValidation` .
`KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `KFoldCrossValidation` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedHoldOutExperiment`.
`RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`RepeatedSubSamplingExperiment(AbstractClassifier... aCs)` Creates a new `RepeatedSubSamplingExperiment` from a set of `AbstractClassifier`s.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `RepeatedSubSamplingExperiment`.
`RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `RepeatedSubSamplingExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)` Creates a new `Sampled_RepeatedHoldOutExperiment` from a set of `AbstractClassifier`s.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)` This constructor allows to assess a collection of given `AbstractClassifier`s and those constructed using the given `TrainableStatisticalModel`s by a `Sampled_RepeatedHoldOutExperiment`.
`Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)` Creates a new `Sampled_RepeatedHoldOutExperiment` from an array of `AbstractClassifier`s and a two-dimensional array of `TrainableStatisticalModel` s, which are combined to additional classifiers.

Uses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased

Subclasses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased
`class` | `ScoreClassifier` This abstract class implements the main functionality of a `DifferentiableSequenceScore` based classifier.

Uses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Subclasses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
`class` | `GenDisMixClassifier` This class implements a classifier the optimizes the following function The weights have to sum to 1.

Uses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

Subclasses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.msp
`class` | `MSPClassifier` This class implements a classifier that allows the training via MCL or MSP principle.

Uses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling

Subclasses of AbstractClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
`class` | `SamplingGenDisMixClassifier` A classifier that samples its parameters from a `LogGenDisMixFunction` using the Metropolis-Hastings algorithm.
`class` | `SamplingScoreBasedClassifier` A classifier that samples the parameters of `SamplingDifferentiableStatisticalModel`s by the Metropolis-Hastings algorithm.

Uses of AbstractClassifier in de.jstacs.classifiers.trainSMBased

Subclasses of AbstractClassifier in de.jstacs.classifiers.trainSMBased
`class` | `TrainSMBasedClassifier` Classifier that works on `TrainableStatisticalModel`s for each of the different classes.

Uses of AbstractClassifier in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Subclasses of AbstractClassifier in de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
`class` | `SharedStructureClassifier` This class enables you to learn the structure on all classes of the classifier together.

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