nu_svr_train.m

各人用Mtalab编写的SVM程序

function svm = Nu_SVR_Train(X,Y,C,nu,ker)

% 输入参数:
% X 训练样本,n×d的矩阵,n为样本个数,d为样本维数
% Y 训练目标,n×1的矩阵,n为样本个数,值为期望输出
% C 拉格朗日乘子上界
% e 不敏感损失函数的参数，Epsilon越大，支持向量越少
% 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:
% ker - 核参数
% x - 训练样本
% y - 训练目标;
% a - 拉格朗日乘子

% ------------------------------------------------------------%
% 解二次优化

n = length(Y);
Q = Calckernel(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!见文件"Nu_SVR_Sim.m"第37,45行
%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);

%第三步：调用优化工具箱quadprog函数求解二次规划
options = optimset;
options.LargeScale = 'off';
options.Display = 'off';

[a,fval,eXitflag,output,lambda] = quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options);
eXitflag

% ------------------------------------------------------------%
% 输出 svm
svm.ker = ker;
svm.x = X;
svm.y = Y;
svm.a = a(1:n)-a(n+1:end);