trains a linear model
model = train(weight_vector, training_label_vector, training_instance_matrix) model = train(weight_vector, training_label_vector, training_instance_matrix, 'liblinear_options') model = train(weight_vector, training_label_vector, training_instance_matrix, 'liblinear_options', 'col')
set type of solver (default 1)
L2-regularized logistic regression (primal)
L2-regularized L2-loss support vector classification (dual)
L2-regularized L2-loss support vector classification (primal)
L2-regularized L1-loss support vector classification (dual)
multi-class support vector classification by Crammer and Singer
L1-regularized L2-loss support vector classification
L1-regularized logistic regression
L2-regularized logistic regression (dual)
set the parameter C (default 1)
set tolerance of termination criterion
|f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2
Dual maximal violation <= eps; similar to libsvm (default 0.1)
|f'(w)|_inf <= eps*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal function (default 0.01)
if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)
weights adjust the parameter C of different classes (see README for details)
n-fold cross validation mode
quiet mode (no outputs)
if 'col' is setted, training_instance_matrix is parsed in column format, otherwise is in row format