- Usage: model = svmtrain(training_label_vector, training_instance_matrix, 'libsvm_options');
- libsvm_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)
- 4 -- precomputed kernel (kernel values in training_instance_matrix)
- -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)
- -v n : n-fold cross validation mode
- -q : quiet mode (no outputs)'
来源: http://lib.csdn.net/snippet/machinelearning/42890