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clear all load allcoor; load base; warning off accu = 0; m=1; %Class=[0,0,0,0]; % 测试过程 for i=1:1:20 for j=4:1:10 %读入测试图像 %filename = sprintf('D:\\MATLAB702\\work\\K-L\\yalefaces

close all; close all; % calc xmean,sigma and its eigen decomposition allsamples=[];%所有训练图像 M=60; N=3; for i=1:1:20 for j=1:1:3 %filename = sprintf('D:\\MATLAB702\\work\\K-L\\yalef

function [xsup,w,d,pos,timeps,alpha,obj]=svmclass(x,y,c,lambda,kernel,kerneloption,verbose,span, alphainit) %svmclass函数用于求解QP问题 span=ones(size(y)); [na,m]=size(span); [n un] = size(y); A = (y*ones(1,m

% Kernel PCA toy example for k(x,y)=exp(-||x-y||^2/rbf_var), cf. Fig. 4 in % @article{SchSmoMue98, % author = "B.~{Sch\"olkopf} and A.~Smola and K.-R.~{M\"uller}", % title = "Nonlinear com

function display(net) % DISPLAY % % Display a textual representation of a support vector classifier object. % % display(net); % % File : @svc/display.m % % Date : Wednesd

function sv = getsv(net) % GETSV % % Accessor method returning the support vectors of a support vector % classifier network. % % sv = getsv(net); % % File : @svc/getsv.m % % D

function w = getw(net) % GETW % % Accessor method returning the weights of a support vector classifier network. % % w = getw(net); % % File : @svc/getw.m % % Date : Tuesd

function bias = getbias(net) % GETBIAS % % Accessor method returning the bias of a support vector classification % network. % % bias = getbias(net); % % File : @svc/getbias.m %

function display(ker) % DISPLAY % % Display a textual representation of a radial basis kernel object. % % display(ker); % % File : @rbf/display.m % % Date : Tuesday 12th

function s = char(ker) % CHAR % % Return a textual representation of a radial basis kernel object. % % str = char(ker); % % File : @rbf/char.m % % Date : Tuesday 12th Spe