%MINC Minimum combining classifier% % 	W = MINC(V)% 	W = V*MINC% % INPUT%   V     Set of classifiers%% OUTPUT%   W    Minimum combining classifier on V%% DESCRIPTION% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the % same classes and W is the maximum combiner: it selects the class % with the maximum 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; ... ]% % SEE ALSO% MAPPINGS, DATASETS, VOTEC, MAXC, MEANC, MEDIANC, PRODC,% AVERAGEC, STACKED, PARALLEL%% EXAMPLES% See PREX_COMBINING% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands % $Id: minc.m,v 1.6 2003/12/19 09:14:38 bob Exp $function w = minc(p1)	type = 'min'; % define the operation processed by FIXEDCC.	% define the name of the combiner. 	% this is the general procedure for all possible calls of fixed combiners	% handled by FIXEDCC	name = 'Minimum combiner'; 	if nargin == 0		w = mapping('fixedcc','combiner',{[],type,name});	else		w = fixedcc(p1,[],type,name);	end	if isa(w,'mapping')		w = setname(w,name);	endreturn