W7编程作业——Support Vector Machines

ex6.m

码住|非作业内容|matlab可以直接训练向量机

model = svmTrain(X, y, C, @linearKernel, 1e-3, 20);
visualizeBoundaryLinear(X, y, model);

gaussianKernel.m

sim=exp(-sum((x1-x2).^2)/(2*sigma^2));

dataset3Params.m //最优参数

C = [0.01,0.03,0.1,0.3,1,3,10,30];
sigma = [0.01,0.03,0.1,0.3,1,3,10,30];
model=zeros(1,64);
predictions=zeros(8,8);
errors=zeros(8,8);
for i=1:8for j=1:8model= svmTrain(X, y, C(i), @(x1, x2) gaussianKernel(x1, x2, sigma(j)));predictions = svmPredict(model, Xval);errors(i,j)=mean(double(predictions ~= yval));      %只要记录下对应的误差值即可end
end
[errors_c,I]=min(errors);
[errors_min,j]=min(errors_c);
i=I(j);
C=C(i);
sigma=sigma(j);

ex6_spam.m //垃圾邮件识别

processEmail.m

for i=1:length(vocabList)if(strcmp(str,vocabList(i)))word_indices = [word_indices ; i];end
end

emailFeatures.m

for i=1:length(word_indices)x(word_indices(i))=1;
end

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W7编程作业——Support Vector Machines

ex6.m

gaussianKernel.m

dataset3Params.m //最优参数

ex6_spam.m //垃圾邮件识别

emailFeatures.m

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