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Accord.Statistics.Models.Markov.Learning Namespace |
Contains learning algorithms such as Baum-Welch.
Classes| Class | Description | |
|---|---|---|
 | BaseBaumWelchLearning | Obsolete.
Base class for implementations of the Baum-Welch learning algorithm.
This class cannot be instantiated.
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| BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions |
Base class for implementations of the Baum-Welch learning algorithm.
This class cannot be instantiated.
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| BaseBaumWelchLearningOptionsTModel, TDistribution, TObservation, TOptions |
Base class for implementations of the Baum-Welch learning algorithm.
This class cannot be instantiated.
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| BaseHiddenMarkovClassifierLearningTClassifier, TModel |
Abstract base class for Sequence Classifier learning algorithms.
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| BaseHiddenMarkovClassifierLearningTClassifier, TModel, TDistribution, TObservation |
Abstract base class for hidden Markov model learning algorithms.
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| BaseHiddenMarkovModelLearningTModel, TObservation |
Base class for implementations of the Baum-Welch learning algorithm.
This class cannot be instantiated.
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| BaseMaximumLikelihoodLearningTModel, TDistribution, TObservation, TOptions |
Base class for observable Markov model learning algorithms.
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| BaseViterbiLearningT |
Base class for implementations of the Viterbi learning algorithm.
This class cannot be instantiated.
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 | BaumWelchLearning |
Baum-Welch learning algorithm for
discrete-density Hidden Markov Models.
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| BaumWelchLearningTDistribution | Obsolete.
Obsolete. Please use BaumWelchLearningTDistribution, TObservation instead.
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| BaumWelchLearningTDistribution, TObservation |
Baum-Welch learning algorithm for
arbitrary-density (generic) Hidden Markov Models.
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| BaumWelchLearningTDistribution, TObservation, TOptions |
Baum-Welch learning algorithms for learning Hidden Markov Models.
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| GenerativeLearningEventArgs |
Submodel learning event arguments.
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| HiddenMarkovClassifierLearning |
Learning algorithm for discrete-density
generative hidden Markov sequence classifiers.
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| HiddenMarkovClassifierLearningTDistribution | Obsolete.
Obsolete. Please use HiddenMarkovClassifierLearningTDistribution, TObservation instead.
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| HiddenMarkovClassifierLearningTDistribution, TObservation |
Learning algorithm for
arbitrary-density generative hidden Markov sequence classifiers.
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| MaximumLikelihoodLearning |
Maximum Likelihood learning algorithm for
discrete-density Hidden Markov Models.
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| MaximumLikelihoodLearningTDistribution | Obsolete.
Obsolete. Please use MaximumLikelihoodLearningTDistribution, TObservation instead.
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| MaximumLikelihoodLearningTDistribution, TObservation |
Maximum Likelihood learning algorithm for discrete-density Hidden Markov Models.
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| ViterbiLearning |
Viterbi learning algorithm.
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| ViterbiLearningTDistribution | Obsolete.
Obsolete. Please use ViterbiLearning<TDistribution, TObservation> instead.
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| ViterbiLearningTDistribution, TObservation |
Viterbi learning algorithm.
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Interfaces| Interface | Description | |
|---|---|---|
 | ISupervisedLearning | Obsolete.
Common interface for supervised learning algorithms for
hidden Markov models such as the
Maximum Likelihood (MLE) learning algorithm.
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| IUnsupervisedLearning | Obsolete.
Common interface for unsupervised learning algorithms for hidden
Markov models such as the Baum-Welch
learning and the Viterbi learning
algorithms.
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| IUnsupervisedLearningT | Obsolete.
Common interface for unsupervised learning algorithms for hidden
Markov models such as the Baum-Welch
learning and the Viterbi learning
algorithms.
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| IWeightedUnsupervisedLearning | Obsolete.
Common interface for unsupervised learning algorithms for hidden
Markov models which support for weighted training samples.
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Delegates| Delegate | Description | |
|---|---|---|
 | ClassifierLearningAlgorithmConfiguration |
Configuration function delegate for Sequence Classifier Learning algorithms.
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