LibSVM

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% 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

Published with MATLAB® 7.9