matlab程序svm四等级分类 支持向量机(SVM)多分类matlab程序代码

%模型训练及数据整理

model_12=svmtrain(class_12_label,class_12_value);

model_13=svmtrain(class_13_label,class_13_value);

model_14=svmtrain(class_14_label,class_14_value);

model_23=svmtrain(class_23_label,class_23_value);

model_24=svmtrain(class_24_label,class_24_value);

model_34=svmtrain(class_34_label,class_34_value);

%??DD?¤2a

[predict_labe1,accuracy_1,dec_values_1] =svmpredict(test_scale_label, test_scale_value, model_12); [predict_labe2,accuracy_2,dec_values_2] =svmpredict(test_scale_label, test_scale_value, model_13); [predict_labe3,accuracy_3,dec_values_3] =svmpredict(test_scale_label, test_scale_value, model_14); [predict_labe4,accuracy_4,dec_values_4] =svmpredict(test_scale_label, test_scale_value, model_23); [predict_labe5,accuracy_5,dec_values_5] =svmpredict(test_scale_label, test_scale_value, model_24); [predict_labe6,accuracy_6,dec_values_6] =svmpredict(test_scale_label, test_scale_value, model_34); predict_labe7=[predict_labe1,predict_labe2,predict_labe3,predict_labe4,predict_labe5,predict_labe 6];

[m,n]=size(predict_labe7);

predict_labe=zeros(m,1);

for i=1:m

n1=0;n2=0;n3=0;n4=0;

for j=1:n

if predict_labe7(i,j)==1

n1=n1+1;

else

if predict_labe7(i,j)==2

n2=n2+1;

else

if predict_labe7(i,j)==3

n3=n3+1;

else

if predict_labe7(i,j)==4

n4=n4+1;

end

end

end

end

end

B=[n1 n2 n3 n4];

max=B(1,1) ;

predict_labe(i,1)=1;

for k=1:3

if max<=B(1,k+1)

max=B(1,k+1);

predict_labe(i,1)=k+1;

end

end