Common interface for convergence-based iterative learning algorithms.
The main algorithms and techniques available on this namespaces are certainly the hidden Markov models. The Accord.NET Framework contains one of the most popular and well-tested offerings for creating, training and validating Markov models using either discrete observations or any arbitrary discrete, continuous or mixed probability distributions to model the observations.
This namespace also brings Conditional Random Fields, that alongside the Markov models can be used to build sequence classifiers, perform gesture recognition, and can even be combined with neural networks to create hybrid models. Other models include regression and survival models.
The namespace class diagram is shown below.
Please note that class diagrams for each of the inner namespaces are also available within their own documentation pages.