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. It contains the class ClassifierAssessment that is used as a super-class of all implemented methodologies of an assessment to assess classifiers.
de.jstacs.classifiers.differentiableSequenceScoreBased	Provides the classes for Classifiers that are based on SequenceScores. It includes a sub-package for discriminative objective functions, namely conditional likelihood and supervised posterior, and a separate sub-package for the parameter priors, that can be used for the supervised posterior.
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of DifferentiableStatisticalModels 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 DifferentiableStatisticalModels either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL).
de.jstacs.classifiers.differentiableSequenceScoreBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.
de.jstacs.classifiers.trainSMBased	Provides the classes for Classifiers that are based on TrainableStatisticalModels.
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()
static AbstractClassifier	ClassifierFactory.createClassifier(DifferentiableSequenceScore... models) Creates a classifier that is based on at least two DifferentiableSequenceScores.
static AbstractClassifier	ClassifierFactory.createClassifier(double[] beta, DifferentiableStatisticalModel... models) Creates a classifier that is based on at least two DifferentiableStatisticalModels.
static AbstractClassifier	ClassifierFactory.createClassifier(LearningPrinciple principle, DifferentiableStatisticalModel... models) Creates a classifier that is based on at least two DifferentiableStatisticalModels.
static AbstractClassifier	ClassifierFactory.createGenerativeClassifier(TrainableStatisticalModel... models) Creates a classifier that is based on at least two TrainableStatisticalModels.

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 AbstractClassifiers.

ClassifierAssessment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and, in addition, classifiers that will be constructed using the given TrainableStatisticalModels.

ClassifierAssessment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new ClassifierAssessment from an array of AbstractClassifiers 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 AbstractClassifiers.

KFoldCrossValidation(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a KFoldCrossValidation .

KFoldCrossValidation(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new KFoldCrossValidation from an array of AbstractClassifiers 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 AbstractClassifiers.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedHoldOutExperiment.

RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedHoldOutExperiment from an array of AbstractClassifiers 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 AbstractClassifiers.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a RepeatedSubSamplingExperiment.

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new RepeatedSubSamplingExperiment from an array of AbstractClassifiers 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 AbstractClassifiers.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.

Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength)
Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers 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 SamplingDifferentiableStatisticalModels 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 TrainableStatisticalModels 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.