Version 2.43 released on February 25, 2021. Installing the Python interface through PyPI is supported
> pip install -U liblinear-officialThe python directory is re-organized so
>>> from liblinear.liblinearutil import *instead of
>>> from liblinearutil import *should be used.
Version 2.42 released on November 1, 2020. For dual CD solvers (logistic/l2 losses but not l1 loss), if a maximal number of iterations is reached, LIBLINEAR directly switches to run a primal Newton solver. See the release note
Multi-core LIBLINEAR is now available to significant speedup the training on shared-memory systems.
We are interested in large sparse regression data. Please let use know if you have some. Thank you.
A practical guide to LIBLINEAR is now available in the end of
Some extensions of LIBLINEAR are at LIBSVM Tools.
LIBLINEAR is the winner of ICML 2008 large-scale learning challenge (linear SVM track). It is also used for winning KDD Cup 2010.
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports
Main features of LIBLINEAR include
FAQ is here
% time libsvm-2.85/svm-train -c 4 -t 0 -e 0.1 -m 800 -v 5 rcv1_train.binary Cross Validation Accuracy = 96.8136% 345.569s % time liblinear-1.21/train -c 4 -e 0.1 -v 5 rcv1_train.binary Cross Validation Accuracy = 97.0161% 2.944sWarning:While LIBLINEAR's default solver is very fast for document classification, it may be slow in other situations. See Appendix C of our SVM guide about using other solvers in LIBLINEAR.
Warning:If you are a beginner and your data sets are not large, you should consider LIBSVM first.
The package includes the source code in C/C++. A README file with detailed explanation is provided. For MS Windows users, there is a subdirectory in the zip file containing binary executable files.
Please read the COPYRIGHT notice before using LIBLINEAR.
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification Journal of Machine Learning Research 9(2008), 1871-1874.
The appendices of this paper give all implementation details of LIBLINEAR.
In the end of this paper there is a practical guide to LIBLINEAR
See also some examples in Appendix C of the SVM guide.
Code used for experiments in our LIBLINEAR papers can be found here.
|Language||Description||Maintainers and Their Affiliation||Supported LIBLINEAR version||Link|
|MATLAB||A simple MATLAB interface||LIBLINEAR authors at National Taiwan University.||The latest||Included in LIBLINEAR package|
|Octave||A simple Octave interface||LIBLINEAR authors at National Taiwan University.||The latest||Included in LIBLINEAR package|
|Java||Java version of LIBLINEAR||Benedikt Waldvogel||1.95||Java LIBLINEAR|
|Python||A python interface has been included in LIBLINEAR since version 1.6.||LIBLINEAR authors at National Taiwan University.||The latest||Included in LIBLINEAR package|
|Ruby||A Ruby interface via SWIG||Kei Tsuchiya (extended from the work of Tom Zeng)||1.93||liblinear-ruby-swig|
|Perl||A Perl interface||Koichi Satoh||The latest||perl module|
|Weka||Weka wrapper||Benedikt Waldvogel||1.5||Weka LIBLINEAR|
|R||R interface to LIBLINEAR||Thibault Helleputte||2.10||R LIBLINEAR|
|Labview||LabView interface to LIBLINEAR. Both Windows/Linux are supported.||Oystein Sture||2.10||LabView interface|
|Common LISP||Common Lisp wrapper of LIBLINEAR||Gábor Melis||1.92||Common LISP wrapper|
|Scilab||Holger Nahrstaedt from the Technical University of Berlin||1.8||Scilab interface|
|PHP||Liblinear for PHP||Matthieu Beurel||The latest||Liblinear for PHP|