Examples of options: -s 0 -c 1000 -t 1 -g 1 -r 1 -d 3
Classify a binary data with polynomial kernel (u'v+1)^3 and C = 1000
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/k) -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 40) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -wi weight: set the parameter C of class i to weight*C in C-SVC (default 1) -v n: n-fold cross validation mode The k in the -g option means the number of attributes in the input data. option -v randomly splits the data into n parts and calculates cross validation accuracy/mean squared error on them.