SVM.rar_SVM人脸分类_SVM人脸识别

function multiSVMStruct = multiSVMTrain(TrainData, nSampPerClass, nClass, C, gamma) %function multiSVMStruct = multiSVMTrain(TrainData, nSampPerClass, nClass, C, gamma) % 采用1对1投票策略将 SVM 推广至多类问题的训练过程，将多类SVM训练结果保存至multiSVMStruct中 % % 输入:--TrainData:每行是一个样本人脸 % --nClass:人数，即类别数 % --nSampPerClass:nClass*1维的向量，记录每类的样本数目，如 nSampPerClass(iClass) % 给出了第iClass类的样本数目 % --C:错误代价系数，默认为 Inf % --gamma:径向基核函数的参数 gamma，默认值为1 % % 输出:--multiSVMStruct:一个包含多类SVM训练结果的结构体 % 默认参数 if nargin < 4 C = Inf; gamma = 1; elseif nargin < 5 gamma = 1; end %开始训练，需要计算每两类间的分类超平面，共(nClass-1)*nClass/2个 for ii=1:(nClass-1) for jj=(ii+1):nClass clear X; clear Y; startPosII = sum( nSampPerClass(1:ii-1) ) + 1; endPosII = startPosII + nSampPerClass(ii) - 1; X(1:nSampPerClass(ii), :) = TrainData(startPosII:endPosII, :); startPosJJ = sum( nSampPerClass(1:jj-1) ) + 1; endPosJJ = startPosJJ + nSampPerClass(jj) - 1; X(nSampPerClass(ii)+1:nSampPerClass(ii)+nSampPerClass(jj), :) = TrainData(startPosJJ:endPosJJ, :); % 设定两两分类时的类标签 Y = ones(nSampPerClass(ii) + nSampPerClass(jj), 1); Y(nSampPerClass(ii)+1:nSampPerClass(ii)+nSampPerClass(jj)) = 0; % 第ii个人和第jj个人两两分类时的分类器结构信息 CASVMStruct{ii}{jj}= svmtrain( X, Y, 'Kernel_Function', @(X,Y) kfun_rbf(X,Y,gamma), 'boxconstraint', C ); end end % 已学得的分类结果 multiSVMStruct.nClass = nClass; multiSVMStruct.CASVMStruct = CASVMStruct; % 保存参数 %save('Mat/params.mat', 'C', 'gamma');