Libsvm有两个参数需要用户指定：c和gamma。实际上在LibSVM中用户需要给出一个c和gamma的区间，LibSVM采用交叉验证cross-validation accuracy的方法确定分类效果最好的c和gamma。

举个例子说明什么是交叉验证，假如把训练样本集拆成三组，然后拿 1 跟 2 来 train model 并 predict 3 以得到正确率； 再来拿 2 跟 3 train 并 predict 1 ，最后 1,3 train 并 predict 2 ，最后取预测精度最高的那组c和gamma。

测试过程：libsvm文件夹下有一个heart_scale测试文件

（1）安装python2.7.6

（2）下载libsvm，进入到tools文件夹下，修改grid.py

（3）gnuplot_exe = r"c:tmpgnuplotbinpgnuplot.exe"  修改gunplot路径

(4) 进入命令行，运行  python grid.py ../heart_scale

运行结束之后，会得到预测精度最高的c和gamma，也就是等高线最高处那时的参数。

但是，仍旧需要进一步调整。

grid.py -log2c 9.25,12.75,0.25 -log2g -11.25,-14.75,-0.25 ../heart_scale

libsvm /windows

生成model的命令：svm-train -c 2048 -g 0.000244140625 ../heart_scale  会得到一个预测model

进行预测的命令：svm-predict ../heart_scale heart_scale.model heart_scale.out

SVM scale：对数据进行规范化 （v-min）/(max-min)

svm-scale -l 0 -u 1 -s german.range german.train >german.train.scale

svm-scale l 0 -u 1 -r german.range german.test > german.test.scale

-b 1是SVM option（具有概率值的预测比没有的结果好）

grid.py -b 1 -v 10 ../heart_sacle

svm-train -b 1 -c g 0.03125 ../heart_scale

svm -predict -b 1 ../heart-scale heart_scale.model

options: -s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/num_features) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 100) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) The k in the -g option means the number of attributes in the input data.

To install this tool, please read the README file in the package. There are Windows, X, and Java versions in the package.

参考：

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

http://www.cnblogs.com/zhangchaoyang/articles/2189606.html

http://baike.baidu.com/link?url=TnWbAaLRe3McQljIQAflAkqyX4ZNZ_B_6X2RoYvJsQrNFKTP7Ts1oVa7xXcoGbxT