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
- -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
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).
