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## 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 `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

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 `DifferentiableStatisticalModel`s.

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.

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