\"                                      Hey, EMACS: -*- nroff -*-
.TH LIBLINEAR-TRAIN 1 "October 21, 2019"
.SH NAME
liblinear-train \- train a linear classifier and produce a model
.SH SYNOPSIS
.B liblinear-train
.RI [ options ] " training_set_file " [ model_file ] 
.br
.SH DESCRIPTION
\fBliblinear-train\fP trains a linear classifier using liblinear and produces a
model suitable for use with \fBliblinear-predict\fP(1).

\fItraining_set_file\fP is the file containing the data used for training.
\fImodel_file\fP is the file to which the model will be saved. If
\fImodel_file\fP is not provided, it defaults to \fItraining_set_file.model\fP.

To obtain good performances, sometimes one needs to scale the data. This can be
done with \fBsvm-scale\fP(1).
.SH OPTIONS
A summary of options is included below.
.TP
.B \-s \fItype\fP
Set the type of the solver (default \fB1\fP):
.sp
.RS 8
.nf
for multi-class classification
.fi
.RE
.sp
.RS 10
.nf
0 ... L2-regularized logistic regression (primal)
.sp
1 ... L2-regularized L2-loss support vector classification (dual)
.sp
2 ... L2-regularized L2-loss support vector classification (primal)
.sp
3 ... L2-regularized L1-loss support vector classification (dual)
.sp
4 ... support vector classification by Crammer and Singer
.sp
5 ... L1-regularized L2-loss support vector classification
.sp
6 ... L1-regularized logistic regression
.sp
7 ... L2-regularized logistic regression (dual)
.sp
.fi
.RE
.RS 8
.nf
for regression
.fi
.RE
.sp
.RS 10
.nf
.sp
11 ... L2-regularized L2-loss support vector regression (primal)
.sp
12 ... L2-regularized L2-loss support vector regression (dual)
.sp
13 ... L2-regularized L1-loss support vector regression (dual)
.sp
.fi
.RE
.RS 8
.nf
for outlier detection
.fi
.RE
.sp
.RS 10
.nf
.sp
21 ... one\-class support vector machine (dual)
.fi
.RE
.TP
.B \-c \fIcost\fP
Set the parameter C (default: \fB1\fP)
.TP
.B \-p \fIepsilon\fP
Set the epsilon in loss function of epsilon-SVR (default: \fB0.1\fP)
.TP
.B \-e \fIepsilon\fP
Set the tolerance of the termination criterion
.TP
.B \-n \fInu\fP
Set the parameter nu of one-class SVM (default 0.5)
.sp
\-s 0 and 2:
.RS 10
.nf
.sp
|f'(w)|_2 <= \fIepsilon\fP*min(pos,neg)/l*|f'(w0)|_2, where f is
the primal function and pos/neg are the number of positive/negative data
(default: \fB0.01\fP)
.fi
.RE
.IP
\-s 11:
.sp
.nf
.RS 10
|f'(w)|_2 <= epsilon*|f'(w0)|_2 (default \fB0.0001\fP)
.fi
.RE
.IP
\-s 1, 3, 4, 7 and 21:
.sp
.nf
.RS 10
Dual maximal violation <= \fIepsilon\fP; similar to libsvm (default: \fB0.1\fP
except 0.01 for \-s 21)
.fi
.RE
.IP
\-s 5 and 6:
.sp
.nf
.RS 10
|f'(w)|_inf <= \fIepsilon\fP*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal
function (default: \fB0.01\fP)
.RE
.IP
\-s 12 and 13:
.sp
.nf
.RS 10
|f'(alpha)|_1 <= epsilon |f'(alpha0)|, where f is the dual function (default \fB0.1\fP)
.RE
.TP
.B \-B \fIbias\fP
If \fIbias\fP >= 0, then instance x becomes [x; bias]; if \fIbias\fP < 0, then
no bias term is added (default: \fB-1\fP)
.TP
.B \-R
not regularize the bias; must with \-B 1 to have the bias; DON'T use this unless you
know what it is (for \-s 0, 2, 5, 6, 11)
.TP
.B \-w\fIi\fP \fIweight\fP
Weights adjust the parameter C of different classes (see README for details)
.TP
.B \-v \fIn\fP
\fIn\fP-fold cross validation mode
.TP
.B \-C
Find parameters (C for \-s 0, 2 and C, p for \-s 11)
.TP
.B \-q
Quiet mode (no outputs).
.fi
.RE
.sp
Option \-v randomly splits the data into n parts and calculates
cross validation accuracy on them.

Option \-C conducts cross validation under different parameters and finds
the best one. This option is supported only by \-s 0, \-s 2 (for finding C)
and \-s 11 (for finding C, p). If the solver is not specified, \-s 2 is used.
.SH EXAMPLES
.sp
Train a linear SVM using L2-loss function:
.sp
.RS 10
.nf
 liblinear-train data_file
.fi
.RE
.sp
Train a logistic regression model:
.sp
.RS 10
.nf
 liblinear-train \-s 0 data_file
.fi
.RE
.sp
Train a linear one\-class SVM which selects roughly 10% data as outliers.
.sp
.RS 10
.nf
 liblinear-train -s 21 -n 0.1 data_file
.fi
.RE
.sp
Do five-fold cross-validation using L2-loss SVM. Use a smaller stopping
tolerance 0.001 than the default 0.1 if you want more accurate solutions:
.sp
.RS 10
.nf
 liblinear-train \-v 5 \-e 0.001 data_file
.fi
.RE
.sp
Conduct cross validation many times by L2-loss SVM and find the parameter
C which achieves the best cross validation accuracy:
.sp
.RS 10
.nf
 train \-C datafile
.fi
.RE
.sp
For parameter selection by \-C, users can specify other
solvers (currently \-s 0, \-s 2 and \-s 11 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 option 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. Similarly, for
\-s 11, users can use the \-p option to specify the
maximal p value of the search range.
.sp
.RS 10
.nf
 train \-C \-s 0 \-v 3 \-c 0.5 \-e 0.0001 datafile
.fi
.RE
.sp
Train four classifiers:
.RS 18
.sp
positive    negative        Cp  Cn
.br
class 1     class 2,3,4     20  10
.br
class 2     class 1,3,4     50  10
.br
class 3     class 1,2,4     20  10
.br
class 4     class 1,2,3     10  10
.RE
.sp
.RS 10
.nf
 liblinear-train \-c 10 \-w1 2 \-w2 5 \-w3 2 four_class_data_file
.fi
.RE
.sp
If there are only two classes, we train ONE model. The C values for the two
classes are 10 and 50:
.sp
.RS 10
.nf
 liblinear-train \-c 10 \-w3 1 \-w2 5 two_class_data_file
.fi
.RE
.sp
Output probability estimates (for logistic regression only) using
\fBliblinear-predict\fP(1):
.sp
.RS 10
.nf
 liblinear-predict \-b 1 test_file data_file.model output_file
.fi
.RE
.SH SEE ALSO
.BR liblinear-predict (1),
.BR svm-predict (1),
.BR svm-train (1),
.BR svm-scale (1)
.SH AUTHORS
liblinear-train was written by the LIBLINEAR authors at National Taiwan
university for the LIBLINEAR Project.
.PP
This manual page was written by Christian Kastner <ckk@debian.org>
for the Debian project (and may be used by others).