liblinear_train说明文档

K>> liblinear_train
Usage: model = train(training_label_vector, training_instance_matrix, 'liblinear_options', 'col');
liblinear_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
-q : quiet mode (no outputs)
col:
    if 'col' is setted, training_instance_matrix is parsed in column format, otherwise is in row format