Usage: train [options] training_set_file [model_file] options: -s type : set type of solver (default 1) for multi-class classification 0 -- L2-regularized logistic regression (primal) 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression 7 -- L2-regularized logistic regression (dual) for regression 11 -- L2-regularized L2-loss support vector regression (primal) 12 -- L2-regularized L2-loss support vector regression (dual) 13 -- L2-regularized L1-loss support vector regression (dual) -c cost : set the parameter C (default 1) -p epsilon : set the epsilon in loss function of SVR (default 0.1) -e epsilon : set tolerance of termination criterion -s 0 and 2 |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2, where f is the primal function and pos/neg are # of positive/negative data (default 0.01) -s 11 |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001) -s 1, 3, 4, and 7 Dual maximal violation <= eps; similar to libsvm (default 0.1) -s 5 and 6 |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1, where f is the primal function (default 0.01) -s 12 and 13 |f'(alpha)|_1 <= eps |f'(alpha0)|, where f is the dual function (default 0.1) -B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) -wi weight: weights adjust the parameter C of different classes (see README for details) -v n: n-fold cross validation mode -C : find parameter C (only for -s 0 and 2) -q : quiet mode (no outputs)

Usage: train [options] training_set_file [model_file] options: -s type : set type of solver (default 1) for multi-class classification 0 -- L2-regularized logistic regression (primal) 1 -- L2-regularized L2-loss support vector classification (dual) 2 -- L2-regularized L2-loss support vector classification (primal) 3 -- L2-regularized L1-loss support vector classification (dual) 4 -- support vector classification by Crammer and Singer 5 -- L1-regularized L2-loss support vector classification 6 -- L1-regularized logistic regression 7 -- L2-regularized logistic regression (dual) for regression 11 -- L2-regularized L2-loss support vector regression (primal) 12 -- L2-regularized L2-loss support vector regression (dual) 13 -- L2-regularized L1-loss support vector regression (dual) -c cost : set the parameter C (default 1) -p epsilon : set the epsilon in loss function of SVR (default 0.1) -e epsilon : set tolerance of termination criterion -s 0 and 2 |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2, where f is the primal function and pos/neg are # of positive/negative data (default 0.01) -s 11 |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001) -s 1, 3, 4, and 7 Dual maximal violation <= eps; similar to libsvm (default 0.1) -s 5 and 6 |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1, where f is the primal function (default 0.01) -s 12 and 13 |f'(alpha)|_1 <= eps |f'(alpha0)|, where f is the dual function (default 0.1) -B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) -wi weight: weights adjust the parameter C of different classes (see README for details) -v n: n-fold cross validation mode -C : find parameter C (only for -s 0 and 2) -q : quiet mode (no outputs)