TrainableStatisticalModel
s, which can
be learned from a single DataSet
.See: Description
Interface | Description |
---|---|
TrainableStatisticalModel |
This interface defines all methods for a probabilistic model.
|
Class | Description |
---|---|
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.
|
PFMWrapperTrainSM |
A wrapper class for representing position weight matrices or position frequency matrices
from databases as
TrainableStatisticalModel s. |
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() . |
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.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.TrainableStatisticalModel
has been trained, it can be used
to compute the likelihood of new sequences.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
.