LibSVM
Téléchargez le zip ici La documentation est recopiée ci-dessous :
% 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/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 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