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Packages that use LearningPrinciple | |
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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. |
Uses of LearningPrinciple in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that return LearningPrinciple | |
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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. |
Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LearningPrinciple | |
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static double[] |
LearningPrinciple.getBeta(LearningPrinciple key)
This method returns the standard weights for a predefined key. |
Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix with parameters of type LearningPrinciple | |
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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. |
Uses of LearningPrinciple in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type LearningPrinciple | |
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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. |
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