%SVM Support vector mapping, kernel PCA% % 	W = svm(A,type,p,n);% % Computes support vector mapping W from the data vectors in A % depending on the value of type:% 	'p': polynomial on inner products with degree p% 	'e': exponential, scaled by p% 	'r': Gaussian radial_basis functions with given stand. dev. p% 	's': sigmoid functions on inner products with given scaling p% 	'd': Euclidean distance ^ p% % If n is given, the mapping is afterwards reduced to n dimensions % by a Karhunen-Loeve reduction (kernel PCA). New objects B can be % mapped by B*W, W*B or by A*svm([],...)*B.% % Defaults: type = 'd', p = 1.% % See also datasets, mapppings, proxm, klm% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlandsfunction w = svm(a,type,p,n);if nargin < 4; n = []; endif nargin < 3; p = 1; endif nargin < 2; type = 'd'; endif nargin < 1 | isempty(a)	w = mapping('svm',{type,p,n});	returnend[m,k] = size(a);if strcmp(type,'s') | strcmp(type,'p')	u = mean(a,1);	aa = a - ones(m,1)*u;else	aa = a;	u = [];endw = mapping('support-vector',{u,aa},getlab(a),k,m,1,{type,p});if ~isempty(n)	w = w*klms(a*w,n);endreturn