perClass Documentation
version 5.1 (31-May-2017)
 SDLINEAR  linear discriminant assuming normal densities

    P=SDLINEAR(DATA,options)

 INPUT
   DATA    labeled dataset

 OUTPUT
   P       linear discriminant (Gaussian model per class, pooled
           covariance matrix)

 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
 SDLINEAR implements linear discriminant assuming that all classes are
 Gaussian with the same covariance matrix.

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

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

 SEE ALSO
 SDQUADRATIC, SDNMEAN, SDGAUSS

sdlinear is referenced in examples: