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

Packages that use LearningPrinciple

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.sampling	Provides the classes for AbstractScoreBasedClassifiers that are based on SamplingDifferentiableStatisticalModels and that sample parameters using the Metropolis-Hastings algorithm.

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

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

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

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

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

Constructors in de.jstacs.classifiers.differentiableSequenceScoreBased.sampling with parameters of type LearningPrinciple

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.