Package org.dkpro.tc.ml.liblinear

Class Summary

Class	Description
LiblinearAdapter	-s type : set type of solver (default 1) 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 -- multi-class 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) 11 -- L2-regularized L2-loss support vector regression (dual) 12 -- L2-regularized L1-loss support vector regression (dual) 13 -- L2-regularized L2-loss support vector regression (primal) -c cost : set the parameter C (default 1) -e epsilon : set tolerance of termination criterion -s 0 and 2 |f'(w)|_2 &lt;= 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 1, 3, 4 and 7 Dual maximal violation <= eps; similar to libsvm (default 0.1) -s 5 and 6 |f'(w)|_inf &le; eps*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal function (default 0.01) -B bias : if bias &ge 0, instance x becomes [x; bias]; if &lt; 0, no bias term added (default -1) -wi weight: weights adjust the parameter C of different classes (see README for details)
LiblinearTestTask