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Accord.Statistics.Models.Markov.Learning Namespace

Contains learning algorithms such as Baum-Welch.
Classes
  ClassDescription
Public classBaseBaumWelchLearning Obsolete.
Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.
Public classBaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions
Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.
Public classBaseBaumWelchLearningOptionsTModel, TDistribution, TObservation, TOptions
Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.
Public classBaseHiddenMarkovClassifierLearningTClassifier, TModel
Abstract base class for Sequence Classifier learning algorithms.
Public classBaseHiddenMarkovClassifierLearningTClassifier, TModel, TDistribution, TObservation
Abstract base class for Sequence Classifier learning algorithms.
Public classBaseHiddenMarkovModelLearningTModel, TObservation
Base class for implementations of the Baum-Welch learning algorithm. This class cannot be instantiated.
Public classBaseViterbiLearningT
Base class for implementations of the Viterbi learning algorithm. This class cannot be instantiated.
Public classCode exampleBaumWelchLearning
Baum-Welch learning algorithm for discrete-density Hidden Markov Models.
Public classBaumWelchLearningTDistribution Obsolete.
Public classCode exampleBaumWelchLearningTDistribution, TObservation
Baum-Welch learning algorithm for arbitrary-density (generic) Hidden Markov Models.
Public classBaumWelchLearningTDistribution, TObservation, TOptions
Baum-Welch learning algorithms for learning Hidden Markov Models.
Public classGenerativeLearningEventArgs
Submodel learning event arguments.
Public classCode exampleHiddenMarkovClassifierLearning
Learning algorithm for discrete-density generative hidden Markov sequence classifiers.
Public classHiddenMarkovClassifierLearningTDistribution Obsolete.
Public classCode exampleHiddenMarkovClassifierLearningTDistribution, TObservation
Public classCode exampleMaximumLikelihoodLearning
Maximum Likelihood learning algorithm for discrete-density Hidden Markov Models.
Public classMaximumLikelihoodLearningTDistribution Obsolete.
Public classCode exampleMaximumLikelihoodLearningTDistribution, TObservation
Maximum Likelihood learning algorithm for discrete-density Hidden Markov Models.
Public classCode exampleViterbiLearning
Viterbi learning algorithm.
Public classViterbiLearningTDistribution Obsolete.
Obsolete. Please use ViterbiLearning<TDistribution, TObservation> instead.
Public classCode exampleViterbiLearningTDistribution, TObservation
Viterbi learning algorithm.
Interfaces
  InterfaceDescription
Public interfaceISupervisedLearning Obsolete.
Common interface for supervised learning algorithms for hidden Markov models such as the Maximum Likelihood (MLE) learning algorithm.
Public interfaceIUnsupervisedLearning Obsolete.
Common interface for unsupervised learning algorithms for hidden Markov models such as the Baum-Welch learning and the Viterbi learning algorithms.
Public interfaceIUnsupervisedLearningT Obsolete.
Common interface for unsupervised learning algorithms for hidden Markov models such as the Baum-Welch learning and the Viterbi learning algorithms.
Public interfaceIWeightedUnsupervisedLearning Obsolete.
Common interface for unsupervised learning algorithms for hidden Markov models which support for weighted training samples.
Delegates
  DelegateDescription
Public delegateClassifierLearningAlgorithmConfiguration
Configuration function delegate for Sequence Classifier Learning algorithms.