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libsvm parameters
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`svm-train' Usage | |
================= | |
Usage: svm-train [options] training_set_file [model_file] | |
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_set_file) | |
-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) |
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