LIBLINEAR-TRAIN(1)

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).