SDPARZEN Parzen density

     P=SDPARZEN(DATA,options)

 INPUT
   DATA     SDDATA set or data matrix

 OUTPUT
   P        Parzen pipeline

 OPTIONS
   h              smoothing parameter (scalar or vector)
   kernel         kernel type
   iter           number of iterations (def: [] = use maximum smooting
                  difference delta to stop)
   delta          maximum smoothing difference (def: 1e-6)
   maxsamples     limit max number of samples used (default: use all)
   prior          class priors (default: use priors from the training set)

 DESCRIPTION
 SDPARZEN implements training of Parzen density estimator or classifier
 using EM algorithm. By default, Laplace kernel is used. Scalar and
 vector smoothing parameters are supported.

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

sdparzen is referenced in examples:

• kb18: How to protect a trained discriminant against outliers?
• kb16: Visualize the effect of a change of parameters in a trained classifier
• kb15: How to speed up classifiers using the neural network approximation?