SYNOPSISliblinear-train [options] training_set_file [model_file]
DESCRIPTIONliblinear-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).
OPTIONSA 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
- Find parameter C (only for -s 0 and 2)
- Quiet mode (no outputs).
Train a linear SVM using L2-loss function:
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
AUTHORSliblinear-train was written by the LIBLINEAR authors at National Taiwan university for the LIBLINEAR Project.
This manual page was written by Christian Kastner <[email protected]>, for the Debian project (and may be used by others).