Created
May 15, 2017 21:39
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liblinear options
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Details of liblinar 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 epsilon-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)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, | |
where f is the primal function (default 0.01) | |
-s 12 and 13\n" | |
|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 | |
-q : quiet mode (no outputs) |
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