svm.rar_svm 图像分类_svm图像分类

function svm = svmTrain(svmType,X,Y,ker,p1,p2) % SVM Classification: % svm = svmTrain('svc_c',x,y,ker,C); % svm = svmTrain('svc_nu',x,y,ker,nu); % % One-Class SVM: % svm = svmTrain('svm_one_class',x,[],ker,nu); % % SVM Regression: % svm = svmTrain('svr_epsilon',x,y,ker,C,e); % svm = svmTrain('svr_nu',x,y,ker,C,nu); % 输入参数: % X 训练样本,d×n的矩阵,n为样本个数,d为样本维数 % Y 训练目标,1×n的矩阵,n为样本个数,值为+1或-1 % ker 核参数(结构体变量) % the following fields: % type - linear : k(x,y) = x'*y % poly : k(x,y) = (x'*y+c)^d % gauss : k(x,y) = exp(-0.5*(norm(x-y)/s)^2) % tanh : k(x,y) = tanh(g*x'*y+c) % degree - Degree d of polynomial kernel (positive scalar). % offset - Offset c of polynomial and tanh kernel (scalar, negative for tanh). % width - Width s of Gauss kernel (positive scalar). % gamma - Slope g of the tanh kernel (positive scalar). % 输出参数: % svm 支持向量机(结构体变量) % the following fields: % type - 支持向量机类型 { 'svc_c','svc_nu','svm_one_class','svr_epsilon','svr_nu' } % ker - 核参数 % x - 训练样本,d×n的矩阵,n为样本个数,d为样本维数 % y - 训练目标,1×n的矩阵,n为样本个数,值为+1或-1 % a - 拉格朗日乘子,1×n的矩阵 % ------------------------------------------------------------% options = optimset; options.LargeScale = 'off'; options.Display = 'off'; switch svmType case 'svc_c', C = p1; n = length(Y); H = (Y'*Y).*kernel(ker,X,X); f = -ones(n,1); A = []; b = []; Aeq = Y; beq = 0; lb = zeros(n,1); ub = C*ones(n,1); a0 = zeros(n,1); [a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); case 'svc_nu', nu = p1; n = length(Y); H = (Y'*Y).*kernel(ker,X,X); f = zeros(n,1); A = -ones(1,n); b = -nu; Aeq = Y; beq = 0; lb = zeros(n,1); ub = ones(n,1)/n; a0 = zeros(n,1); [a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); case 'svm_one_class', nu = p1; n = size(X,2); H = kernel(ker,X,X); f = zeros(n,1); for i = 1:n f(i,:) = -kernel(ker,X(:,i),X(:,i)); end A = []; b = []; Aeq = ones(1,n); beq = 1; lb = zeros(n,1); ub = ones(n,1)/(nu*n); a0 = zeros(n,1); [a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); case 'svr_epsilon', C = p1; e = p2; n = length(Y); Q = kernel(ker,X,X); H = [Q,-Q;-Q,Q]; f = [e*ones(n,1)-Y';e*ones(n,1)+Y']; % 符号不一样,决策函数就不一样,实际上是一回事! %f = [e*ones(n,1)+Y';e*ones(n,1)-Y']; A = []; b = []; Aeq = [ones(1,n),-ones(1,n)]; beq = 0; lb = zeros(2*n,1); ub = C*ones(2*n,1); a0 = zeros(2*n,1); [a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); a = a(1:n)-a(n+1:end); case 'svr_nu', C = p1; nu = p2; n = length(Y); Q = kernel(ker,X,X); H = [Q,-Q;-Q,Q]; f = [-Y';+Y']; % 符号不一样,决策函数就不一样,实际上是一回事! %f = [+Y';-Y']; A = []; b = []; Aeq = [ones(1,n),-ones(1,n);ones(1,2*n)]; beq = [0;C*n*nu]; lb = zeros(2*n,1); ub = C*ones(2*n,1); a0 = zeros(2*n,1); [a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); a = a(1:n)-a(n+1:end); otherwise, end eXitflag % ------------------------------------------------------------% % 输出 svm svm.type = svmType; svm.ker = ker; svm.x = X; svm.y = Y; svm.a = a';