MATLAB中10倍SVM分类的示例

我需要一个某种描述性的例子,展示如何对两类数据进行10倍SVM分类.在MATLAB文档中只有一个例子,但它不是10倍.有人能帮我吗？

1> Amro..：

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这里有一个完整的例子,使用从生物信息工具箱以下功能:svmtrain去,SVMCLASSIFY,CLASSPERF,CROSSVALIND.

load fisheriris                              %# load iris dataset
groups = ismember(species,'setosa');         %# create a two-class problem

%# number of cross-validation folds:
%# If you have 50 samples, divide them into 10 groups of 5 samples each,
%# then train with 9 groups (45 samples) and test with 1 group (5 samples).
%# This is repeated ten times, with each group used exactly once as a test set.
%# Finally the 10 results from the folds are averaged to produce a single 
%# performance estimation.
k=10;

cvFolds = crossvalind('Kfold', groups, k);   %# get indices of 10-fold CV
cp = classperf(groups);                      %# init performance tracker

for i = 1:k                                  %# for each fold
    testIdx = (cvFolds == i);                %# get indices of test instances
    trainIdx = ~testIdx;                     %# get indices training instances

    %# train an SVM model over training instances
    svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ...
                 'Autoscale',true, 'Showplot',false, 'Method','QP', ...
                 'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);

    %# test using test instances
    pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false);

    %# evaluate and update performance object
    cp = classperf(cp, pred, testIdx);
end

%# get accuracy
cp.CorrectRate

%# get confusion matrix
%# columns:actual, rows:predicted, last-row: unclassified instances
cp.CountingMatrix

与输出:

ans =
      0.99333
ans =
   100     1
     0    49
     0     0

99.33%只有一个'setosa'实例被错误归类为'non-setosa',我们获得了准确性

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更新:SVM功能已移至R2013a中的统计工具箱

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@TARIQ:有点偏离主题,但你可以简单地使用`bar3`来绘制混淆矩阵.如果你有神经网络工具箱,那就是[`plotconfusion`](http://www.mathworks.com/help/nnet/ref/plotconfusion.html)函数,否则你可以像这样手动执行:http:/ /stackoverflow.com/a/7081430/97160