Bayes classifier based on given density estimates
W = BAYESC(WA,WB, ... ,P,LABLIST)
The trained mappings WA,WB, ... should supply proper densities estimates D for a dataset X by D = X*WA, etcetera. E.g. they should be trained by commands like GAUSSM(A), PARZENM(A), KNNM(A). Consequently, they should have a size of K x 1 (assuming that X and A are K-dimensional). Also sizes of K x N are supported, assuming a combined density estimate for N classes simultaneously. BAYESC weighs the class densitites by the class priors in P and names the classes by LABLIST. If LABLIST is not supplied, the labels stored in the mappings are used.
1. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd edition, John Wiley and Sons, New York, 2001.