%BAGGINGC Bootstrapping and aggregation of classifiers% %    W = BAGGINGC (A,CLASSF,N,ACLASSF,T)% % INPUT%   A         Training dataset.%   CLASSF    The base classifier (default: nmc)%   N         Number of base classifiers to train (default: 100)%   ACLASSF   Aggregating classifier (default: nmc), [] for no aggregation.%   T         Tuning set on which ACLASSF is trained (default: [], meaning use A)%% OUTPUT%    W        A combined classifier (if ACLASSF was given) or a stacked%             classifier (if ACLASSF was []).%% DESCRIPTION% Computation of a stabilised version of a classifier by bootstrapping and% aggregation ('bagging'). In total N bootstrap versions of the dataset A% are generated and used for training of the untrained classifier CLASSF.% Aggregation is done using the combining classifier specified in CCLASSF.% If ACLASSF is a trainable classifier it is trained by the tuning dataset% T, if given; else A is used for training. The default aggregating classifier% ACLASSF is MEANC. Default input classifier CLASSF is NMC.%% Note that the classifier combiner MEANC averages the coefficients of the% affine linear input classifiers, e.g. W = baggingc(A,nmc); This can be% avoided by using W = baggingc(A,nmc*classc), which will average posterior% probability outputs.% % SEE ALSO% DATASETS, MAPPINGS, NMC, MEANC, BOOSTINGC% 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: baggingc.m,v 1.6 2005/02/01 15:53:58 duin Exp $function w = baggingc (a,clasf,n,rule,t)	prtrace(mfilename);	if (nargin < 5), 		prwarning(2,'no tuning set supplied, using training set for tuning (risk of overfit)');		t = []; 	end	if (nargin < 4)		prwarning(2,'aggregating classifier not specified, assuming nmc'); 		rule = meanc; 	end	if (nargin < 3) | isempty(n),		prwarning(2,'number of repetitions not specified, assuming 100');		n = 100; 	end	if (nargin < 2) | isempty(clasf),		prwarning(2,'base classifier not specified, assuming nmc');		clasf = nmc; 	end	if ((nargin < 1) | isempty(a))		w = mapping('baggingc',{clasf,n,rule});		return	end	iscomdset(a,t); % test compatibility training and tuning set		% Concatenate N classifiers on bootstrap samples (100%) taken	% from the training set.	w = [];	for i = 1:n		w = [w gendat(a)*clasf]; 	end	% If no aggregating classifier is given, just return the N classifiers...	if (~isempty(rule))		% ... otherwise, train the aggregating classifier on the train or		% tuning set.		if (isempty(t))			w = traincc(a,w,rule);		else			w = traincc(t,w,rule);		end	end	w = setcost(w,a);		return