|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES All Classes |
TrainableStatisticalModel
s, which can
be learned from a single DataSet
.
See:
Description
Interface Summary | |
---|---|
TrainableStatisticalModel | This interface defines all methods for a probabilistic model. |
Class Summary | |
---|---|
AbstractTrainableStatisticalModel | Abstract class for a model for pattern recognition. |
CompositeTrainSM | This class is for modelling sequences by modelling the different positions of the each sequence by different models. |
DifferentiableStatisticalModelWrapperTrainSM | This model can be used to use a DifferentiableStatisticalModel as model. |
TrainableStatisticalModelFactory | This class allows to easily create some frequently used models. |
UniformTrainSM | This class represents a uniform model. |
VariableLengthWrapperTrainSM | This class allows to train any TrainableStatisticalModel on DataSet s of Sequence s with
variable length if each individual length is at least SequenceScore.getLength() . |
Provides all TrainableStatisticalModel
s, which can
be learned from a single DataSet
. Often, parameter learning follows a learning principle
like maximum likelihood or maximum a-posteriori. Parameter learning typically is performed analytically like for the homogeneous and inhomogeneous
models in the de.jstacs.sequenceScores.statisticalModels.trainable.discrete
sub-package.
Notable exceptions are hidden Markov models (de.jstacs.sequenceScores.statisticalModels.trainable.hmm
), which are learned by Baum-Welch or Viterbi training,
and mixture models (de.jstacs.sequenceScores.statisticalModels.trainable.mixture
), which are learned by expectation-maximization (EM) or
Gibbs sampling.
After a TrainableStatisticalModel
has been trained, it can be used
to compute the likelihood of new sequences.
Any combination of TrainableStatisticalModel
s can be used to build a
TrainSMBasedClassifier
, which can be used to classify new sequences and which can
be evaluated using a ClassifierAssessment
.
|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES All Classes |