Minimum combining classifier
W = MINC(V)
If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same classes and W is the minimum combiner: it selects the class with the minimum of the outputs of the input classifiers. This might also be used as A*[V1,V2,V3]*MINC in which A is a dataset to be classified. Consequently, if S is a dissimilarity matrix with class feature labels (e.g. S = A*PROXM(A,'d')) then S*MINC*LABELD is the nearest neighbor classifier.
If it is desired to operate on posterior probabilities then the input classifiers should be extended like V = V*CLASSC;
The base classifiers may be combined in a stacked way (operating in the same feature space by V = [V1,V2,V3, ... ] or in a parallel way (operating in different feature spaces) by V = [V1;V2;V3; ... ]
mappings, datasets, votec, maxc, meanc, medianc, prodc, averagec, stacked, parallel,