Trainable classifier based on Mixture of Gaussians
W = MOGC(A,N)
For each class j in A a density estimate is made by GAUSSM, using N(j) mixture components. Using the class prior probabilities they are combined into a single classifier W. If N is a scalar, this number is applied to all classes. The relative size of the components is stored in W.DATA.PRIOR.
For small class sizes or for large values of N it may be difficult or impossible to find the desired number of components. This may result in relatively long computing times.