Nearest Mean Scaled Classifier
W = NMSC(A)
Computation of the linear discriminant for the classes in the dataset A assuming normal distributions with zero covariances and equal class variances. The use of soft labels is supported.
The difference with NMC is that NMSC is based on an assumption of normal distributions and thereby automatically scales the features and is sensitive to class priors. NMC is a plain nearest mean classifier that is feature scaling sensitive and unsensitive to class priors.
As NMSC is a linear classifier, a non-linear combiner might give an improvement in multi-dimensional problems, e.g. by W = A*(NMC*QDC(,,1e-6)).