Uses of Class
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix.GenDisMixClassifier

Packages that use GenDisMixClassifier

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.

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix that return GenDisMixClassifier

GenDisMixClassifier	GenDisMixClassifier.clone()
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 DifferentiableStatisticalModels.

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

Subclasses of GenDisMixClassifier in de.jstacs.classifiers.differentiableSequenceScoreBased.msp

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

Methods in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling that return GenDisMixClassifier

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.