SDMIXTURE Estimating Gaussian mixture model

     P=SDMIXTURE(DATA,options)

 INPUT
   DATA     data set object

 OUTPUT
   P        mixture model pipeline

 OPTIONS
  'comp'        number of components per class (def: 'auto' = choose automatically)
  'maxsamples'  maximum number of samples used for initialization (def: 500)
  'iter'        number of iterations (def: 30). If iter==[], use the likelihood delta
                to stop
  'delta'       likelihood change to use as stopping crit (def: 1e-4)
  'cluster'     return clustering result (one output per component)
  'prior'       class priors (default: use priors from the training set)

 DESCRIPTION
 SDMIXTURE estimate Gaussian mixture model for each class in DATA.
 By default, the number of mixture components is estimated from data
 based on non-parameteric density estimator. Number of components may
 be also provided in the 'comp' option.
 SDMIXTURE may be used for unsupervised cluster analysis using the
 'cluster' option. If specified, SDMIXTURE will perform mixture
 estimation on each class present and return one output per cluster.

 READ MORE
 http://perclass.com/doc/guide/classifiers.html#sdmixture

 SEE ALSO
 SDGAUSS, SDPARZEN

sdmixture is referenced in examples:

• kb19: PRTools compatibility
• kb18: How to protect a trained discriminant against outliers?
• kb11: Hierarchical classifier: How to build detector-classifier cascade?
• kb10: A step by step construction of a detector