supportvectortrain1.m

支持向量机的手写数字识别,绝对原创,识别率100％

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%函数名称：SupportVectorTrain1
%参数：
%返回值：
%函数功能：支持向量机训练
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function SupportVectorTrain1();
% change
% change
    %选择核函数类型
    str={'线性核函数','二次核函数','多项式核函数','rbf核函数','多层感知器核函数'};
    kernalType=listdlg('ListString',str,'PromptString','选择核函数计算类型', 'SelectionMode','Single','ListSize',[160,100],'Name','核函数选择对话框');
    switch(kernalType)
        case 1
            kernal='linear';
        case 2
            kernal='quadratic';
        case 3
            kernal='polynomial';
        case 4
            kernal='rbf';
        case 5
            kernal='mlp';
    end
    load pattern;                 % change
    n = length(pattern);
    for i = 1:n-1                 % change
        for j= i+1:n                 % change
            x = [pattern(i).feature;pattern(j).feature];                 % change
            y = ones(1,pattern(i).patternNum + pattern(j).patternNum)';                 % change
            y( (pattern(i).patternNum  + 1) : (pattern(i).patternNum + pattern(j).patternNum) ) = -1;     % change
            %进行两类支持向量机训练，结果保存到svmStruct中
            svmStruct(i,j)=svmtrain(x,y,'Kernel_Function',kernal);
        end
    end
    %保存训练结果
    save svmStruct;                 % change
    msgbox('训练结束');