Uses of Class
de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifier

Packages that use GenDisMixClassifier

de.jstacs.classifier.scoringFunctionBased.gendismix	Provides an implementation of a classifier that allows to train the parameters of a set of NormalizableScoringFunctions by a unified generative-discriminative learning principle
de.jstacs.classifier.scoringFunctionBased.msp	Provides an implementation of a classifier that allows to train the parameters of a set of ScoringFunctions either by maximum supervised posterior (MSP) or by maximum conditional likelihood (MCL)
de.jstacs.classifier.scoringFunctionBased.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingScoringFunctions and that sample parameters using the Metropolis-Hastings algorithm.

Uses of GenDisMixClassifier in de.jstacs.classifier.scoringFunctionBased.gendismix

Methods in de.jstacs.classifier.scoringFunctionBased.gendismix that return GenDisMixClassifier

GenDisMixClassifier	GenDisMixClassifier.clone()
static GenDisMixClassifier[]	GenDisMixClassifier.create(GenDisMixClassifierParameterSet params, LogPrior prior, double[] weights, NormalizableScoringFunction[]... functions) This method creates an array of GenDisMixClassifiers by using the cross-product of the given NormalizableScoringFunctions.

Uses of GenDisMixClassifier in de.jstacs.classifier.scoringFunctionBased.msp

Subclasses of GenDisMixClassifier in de.jstacs.classifier.scoringFunctionBased.msp

class

MSPClassifier This class implements a classifier that allows the training via MCL or MSP principle.

Uses of GenDisMixClassifier in de.jstacs.classifier.scoringFunctionBased.sampling

Methods in de.jstacs.classifier.scoringFunctionBased.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.