tu_haberman.m

matlab编写的SVM分类器源代码

y_AL1=[ 0.7091
    0.7091
    0.7091
    0.7091
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6727
    0.6727
    0.6727
    0.6727
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.7091
    0.7273];
y_AL1=y_AL1';
y_AL2=[ 0.7273
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091];
y_AL2=y_AL2';
y_AL3=[ 0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7273
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7455];
y_AL3=y_AL3';
y_AL=(y_AL1+y_AL2+y_AL3)./3;
x_AL=0:1:20;
y_RS1=[0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909
    0.6909];
y_RS1=y_RS1';
y_RS2=[0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091
    0.7091];
y_RS2=y_RS2';
y_RS3=[0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.7273
    0.6727
    0.6727
    0.7091
    0.7091
    0.6909
    0.6909
    0.6909
    0.6909
    0.7091];
y_RS3=y_RS3';
y_RS=(y_RS1+y_RS2+y_RS3)./3;
x_RS=0:1:20;
plot(x_AL,y_AL,'b:',x_RS,y_RS,'r-')
xlabel('Number of Labels');
ylabel('Accuracy');
title('针对haberman数据集主动学习与随机采样的对比学习曲线');
legend('Uncertainty','Random');