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Packages that use GenDisMixClassifier | |
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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.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. |
Uses of GenDisMixClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that return GenDisMixClassifier | |
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GenDisMixClassifier |
GenDisMixClassifier.clone()
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static GenDisMixClassifier[] |
GenDisMixClassifier.create(GenDisMixClassifierParameterSet params,
LogPrior prior,
double[] weights,
DifferentiableStatisticalModel[]... functions)
This method creates an array of GenDisMixClassifiers by using the cross-product of the given DifferentiableStatisticalModel s. |
Uses of GenDisMixClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.msp |
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Subclasses of GenDisMixClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.msp | |
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class |
MSPClassifier
This class implements a classifier that allows the training via MCL or MSP principle. |
Uses of GenDisMixClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling |
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Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that return GenDisMixClassifier | |
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GenDisMixClassifier |
SamplingGenDisMixClassifier.getClassifierForBestParameters(GenDisMixClassifierParameterSet params)
Returns a standard, i.e., non-sampling, GenDisMixClassifier , where the parameters
are set to those that yielded the maximum value of the objective functions among all sampled
parameter values. |
GenDisMixClassifier |
SamplingGenDisMixClassifier.getClassifierForMeanParameters(GenDisMixClassifierParameterSet params,
boolean testBurnIn,
int minBurnInSteps)
Returns a standard, i.e., non-sampling, GenDisMixClassifier , where the parameters
are set to the mean values over all sampled
parameter values in the stationary phase. |
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