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Uses of Package
de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix

Packages that use de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
de.jstacs.classifiers This package provides the framework for any classifier. 
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. 
 

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix used by de.jstacs.classifiers
LearningPrinciple
          This enum can be used to obtain the weights for well-known optimization tasks.
 

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix used by de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix
GenDisMixClassifier
          This class implements a classifier the optimizes the following function
\[f(\underline{\lambda}|C,D,\underline{\alpha},\underline{\beta})
The weights $\beta_i$ have to sum to 1.
GenDisMixClassifierParameterSet
          This class contains the parameters for the GenDisMixClassifier.
LearningPrinciple
          This enum can be used to obtain the weights for well-known optimization tasks.
LogGenDisMixFunction
          This class implements the the following function
\[f(\underline{\lambda}|C,D,\underline{\alpha},\underline{\beta})
The weights $\beta_i$ have to sum to 1.
 

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix used by de.jstacs.classifiers.differentiableSequenceScoreBased.msp
GenDisMixClassifier
          This class implements a classifier the optimizes the following function
\[f(\underline{\lambda}|C,D,\underline{\alpha},\underline{\beta})
The weights $\beta_i$ have to sum to 1.
GenDisMixClassifierParameterSet
          This class contains the parameters for the GenDisMixClassifier.
 

Classes in de.jstacs.classifiers.differentiableSequenceScoreBased.gendismix used by de.jstacs.classifiers.differentiableSequenceScoreBased.sampling
GenDisMixClassifier
          This class implements a classifier the optimizes the following function
\[f(\underline{\lambda}|C,D,\underline{\alpha},\underline{\beta})
The weights $\beta_i$ have to sum to 1.
GenDisMixClassifierParameterSet
          This class contains the parameters for the GenDisMixClassifier.
LearningPrinciple
          This enum can be used to obtain the weights for well-known optimization tasks.
 

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