Package | Description |
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de.jstacs.classifiers |
This package provides the framework for any classifier.
|
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.sampling |
Provides the classes for
AbstractScoreBasedClassifier s that are based on
SamplingDifferentiableStatisticalModel s
and that sample parameters using the Metropolis-Hastings algorithm. |
Modifier and Type | Method and Description |
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static AbstractClassifier |
ClassifierFactory.createClassifier(LearningPrinciple principle,
DifferentiableStatisticalModel... models)
Creates a classifier that is based on at least two
DifferentiableStatisticalModel s. |
Modifier and Type | Method and Description |
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static LearningPrinciple |
LearningPrinciple.valueOf(String name)
Returns the enum constant of this type with the specified name.
|
static LearningPrinciple[] |
LearningPrinciple.values()
Returns an array containing the constants of this enum type, in
the order they are declared.
|
Modifier and Type | Method and Description |
---|---|
static double[] |
LearningPrinciple.getBeta(LearningPrinciple key)
This method returns the standard weights for a predefined key.
|
Constructor and Description |
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GenDisMixClassifier(GenDisMixClassifierParameterSet params,
LogPrior prior,
LearningPrinciple key,
DifferentiableStatisticalModel... score)
This convenience constructor creates an array of weights for an elementary learning principle and calls the main constructor.
|
Constructor and Description |
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SamplingGenDisMixClassifier(SamplingGenDisMixClassifierParameterSet params,
BurnInTest burnInTest,
double[] classVariances,
LogPrior prior,
LearningPrinciple principle,
SamplingDifferentiableStatisticalModel... scoringFunctions)
Creates a new
SamplingGenDisMixClassifier using the external parameters
params , a burn-in test, a set of sampling variances for the different classes,
a prior on the parameters, a learning principle,
and scoring functions that model the distribution for each of the classes. |