xenial (1) liblinear-train.1.gz

NAME

       liblinear-train - train a linear classifier and produce a model

SYNOPSIS

       liblinear-train [options] training_set_file [model_file]

DESCRIPTION

       liblinear-train  trains  a  linear  classifier using liblinear and produces a model suitable for use with
       liblinear-predict(1).

       training_set_file is the file containing the data used for training.  model_file is the file to which the
       model will be saved. If model_file is not provided, it defaults to training_set_file.model.

       To obtain good performances, sometimes one needs to scale the data. This can be done with svm-scale(1).

OPTIONS

       A summary of options is included below.

       -s type
              Set the type of the solver:

                 0 ... L2-regularized logistic regression

                 1 ... L2-regularized L2-loss support vector classification (dual) (default)

                 2 ... L2-regularized L2-loss support vector classification (primal)

                 3 ... L2-regularized L1-loss support vector classification (dual)

                 4 ... multi-class support vector classification

                 5 ... L1-regularized L2-loss support vector classification

                 6 ... L1-regularized logistic regression

                 7 ... L2-regularized logistic regression (dual)

       -c cost
              Set the parameter C (default: 1)

       -e epsilon
              Set the tolerance of the termination criterion

              For -s 0 and 2:

                 |f'(w)|_2 <= epsilon*min(pos,neg)/l*|f'(w0)_2, where f is
                 the primal function and pos/neg are the number of positive/negative data
                 (default: 0.01)

              For -s 1, 3, 4 and 7:

                 Dual maximal violation <= epsilon; similar to libsvm (default: 0.1)

              For -s 5 and 6:

                 |f'(w)|_inf <= epsilon*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal
                 function (default: 0.01)

       -B bias
              If bias >= 0, then instance x becomes [x; bias]; if bias < 0, then
              no bias term is added (default: -1)

       -wi weight
              Weight-adjusts the parameter C of class i by the value weight

       -v n   n-fold cross validation mode

       -C     Find parameter C (only for -s 0 and 2)

       -q     Quiet mode (no outputs).

EXAMPLES

       Train a linear SVM using L2-loss function:

                  liblinear-train data_file

       Train a logistic regression model:

                  liblinear-train -s 0 data_file

       Do  five-fold cross-validation using L2-loss SVM, using a smaller stopping tolerance 0.001 instead of the
       default 0.1 for more accurate solutions:

                  liblinear-train -v 5 -e 0.001 data_file

       Conduct cross validation many times by L2-loss SVM and find the parameter C which achieves the best cross
       validation accuracy:

                  train -C datafile

       For  parameter  selection  by -C, users can specify other solvers (currently -s 0 and -s 2 are supported)
       and different number of CV folds. Further, users can use the -c option to specify the smallest C value of
       the  search range. This setting is useful when users want to rerun the parameter selection procedure from
       a specified C under a different setting, such as a stricter stopping tolerance -e  0.0001  in  the  above
       example.

                  train -C -s 0 -v 3 -c 0.5 -e 0.0001 datafile

       Train four classifiers:

                         positive    negative        Cp  Cn
                         class 1     class 2,3,4     20  10
                         class 2     class 1,3,4     50  10
                         class 3     class 1,2,4     20  10
                         class 4     class 1,2,3     10  10

                  liblinear-train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file

       If there are only two classes, we train ONE model. The C values for the two classes are 10 and 50:

                  liblinear-train -c 10 -w3 1 -w2 5 two_class_data_file

       Output probability estimates (for logistic regression only) using liblinear-predict(1):

                  liblinear-predict -b 1 test_file data_file.model output_file

SEE ALSO

       liblinear-predict(1), svm-predict(1), svm-train(1)

AUTHORS

       liblinear-train  was  written  by  the  LIBLINEAR authors at National Taiwan university for the LIBLINEAR
       Project.

       This manual page was written by Christian Kastner <debian@kvr.at>, for the Debian  project  (and  may  be
       used by others).

                                                 March 08, 2011                               LIBLINEAR-TRAIN(1)