IUnsupervisedLearning Interface

Note: This API is now obsolete.

Common interface for unsupervised learning algorithms for hidden Markov models such as the Baum-Welch learning and the Viterbi learning algorithms.

Namespace: Accord.Statistics.Models.Markov.Learning
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

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[ObsoleteAttribute("Please use Accord.MachineLearning.IUnsupervisedLearning instead.")]
public interface IUnsupervisedLearning

<ObsoleteAttribute("Please use Accord.MachineLearning.IUnsupervisedLearning instead.")>
Public Interface IUnsupervisedLearning

The IUnsupervisedLearning type exposes the following members.

Methods

	Name	Description
	Run	Runs the learning algorithm.

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Remarks

In the context of hidden Markov models, unsupervised algorithms are algorithms which consider that the sequence of states in a system is hidden, and just the system's outputs can be seen (or are known) during training. This is in contrast with supervised learning algorithms such as the Maximum Likelihood (MLE), which consider that both the sequence of observations and the sequence of states are observable during training.

Reference

Accord.Statistics.Models.Markov.Learning Namespace