MATLAB R2014a 装 limsvm-3.17

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1.下载libsvm

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

在libsvm的网站上下载 libsvm-3.12.zip文件，解压后放在任意目录下，最好放在MATLAB工具箱中，比如 D:\program files (x86)\MATLAB\R2014a\toolbox\libsvm-3.22下。

2.打开matlab，添加libsvm的目录，如下图所示

3.MATLAB编译器下，命令窗口输入 mex -setup ，前提本机装有visual studio编译器

4.编译生成文件

命令窗口输入：make。输入make，会有提示找不到 svmtrain.exp svmpredict.exp，没关系。只要在libsvm/matlab目录下生成了这4个文件libsvmread.mexw32，libsvmwrite.mexw32，svmtrain.mexw32，svmpredict.mexw32 就行了。

5.测试

5.1

>> load heart_scale;

错误，去下载heart_scale.mat文件放在libsvm-3.22文件下

改为load('heart_scale.mat')

5.2

>> model = svmtrain(heart_scale_label, heart_scale_inst);

输出*
optimization finished, #iter = 162
nu = 0.431029
obj = -100.877288, rho = 0.424462
nSV = 132, nBSV = 107
Total nSV = 132

5.3

>> [predict_label,accuracy] = svmpredict(heart_scale_label,heart_scale_inst,model);

错误弹出

Usage: [predicted_label, accuracy, decision_values/prob_estimates] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')
[predicted_label] = svmpredict(testing_label_vector, testing_instance_matrix, model, 'libsvm_options')
Parameters:
model: SVM model structure from svmtrain.
libsvm_options:
-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet
-q : quiet mode (no outputs)
Returns:
predicted_label: SVM prediction output vector.
accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.
prob_estimates: If selected, probability estimate vector.

格式错误

改为

>> [predict_label, accuracy,decision_values ] = svmpredict(heart_scale_label, heart_scale_inst, model,'-b 0');

输出

Accuracy = 86.6667% (234/270) (classification)

表示测试成功，安装成功

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