EM Class 
Namespace: Emgu.CV.ML
The EM type exposes the following members.
Name  Description  

ClustersNumber 
The number of mixtures
 
CovarianceMatrixType 
The type of the mixture covariation matrices
 
Ptr 
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)  
TermCriteria 
Termination criteria of the procedure. EM algorithm stops either after a certain number of iterations (term_crit.num_iter), or when the parameters change too little (no more than term_crit.epsilon) from iteration to iteration

Name  Description  

Dispose 
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.)  
DisposeObject 
Release the memory associated with this EM model
(Overrides DisposableObjectDisposeObject.)  
Equals  (Inherited from Object.)  
Finalize 
Destructor
(Inherited from DisposableObject.)  
GetHashCode  (Inherited from Object.)  
GetType  (Inherited from Object.)  
MemberwiseClone  (Inherited from Object.)  
Predict 
Predict the probability of the samples  
ReleaseManagedResources 
Release the managed resources. This function will be called during the disposal of the current object.
override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.)  
ToString  (Inherited from Object.)  
trainE 
Estimate the Gaussian mixture parameters from a samples set. This variation starts with Expectation step. You need to provide initial means of mixture components. Optionally you can pass initial weights and covariance matrices of mixture components.
 
TrainM 
Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be estimated by the kmeans algorithm.
Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure, and optionally computes the output "class label" for each sample.
The trained model can be used further for prediction, just like any other classifier.

Name  Description  

_ptr 
A pointer to the unmanaged object
(Inherited from UnmanagedObject.) 
Name  Description  

Clear 
Clear the statistic model
(Defined by StatModelExtensions.)  
Predict 
Predicts response(s) for the provided sample(s)
(Defined by StatModelExtensions.)  
Read 
Reads algorithm parameters from a file storage.
(Defined by AlgorithmExtensions.)  
Save 
Save the statistic model to file
(Defined by StatModelExtensions.)  
Train(TrainData, Int32)  Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)  
Train(IInputArray, DataLayoutType, IInputArray)  Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)  
Write 
Stores algorithm parameters in a file storage
(Defined by AlgorithmExtensions.) 