perClass Documentation
version 5.1 (31-May-2017)
 SDQUADRATIC  Quadratic discriminant assuming normal densities

    P=SDQUADRATIC(DATA,options)

 INPUT
   DATA     Labeled dataset (with two or more classes)

 OUTPUT
   P        Quadratic discriminant (Gaussian model per class)

 OPTIONS
   'prior'    Class priors (default: use priors from the training set)
   'no display'  Do not show progress of regularization optimization
  Regularization:
   'reg'      Automatic regularization
   'reg',R    Regularization constant added to diagonal
   'test',TS  Use a test/validation set TS to evaluate regularization
              Do not split DATA internally.
   'tsfrac',F Fraction of data used to validating error (default: 0.2)

 DESCRIPTION
 SDQUADRATIC implements quadratic discriminant assuming Gaussian
 distributions. For each class a Gaussian model is estimated with full
 covariance matrix.

 EXAMPLES
 p=sdquadratic(data)   % Train gaussian model, no regularization
 p=sdquadratic(data,'reg')   % run automatic regularization
 p=sdquadratic(data,'reg',0.01)   % regularize by adding 0.01 on cov.diagonal

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

 SEE ALSO
 SDGAUSS, SDLINEAR, SDMIXTURE, SDNMEAN

sdquadratic is referenced in examples: