Version 1.96 released on November 15, 2014. It conducts some minor fixes. Also note that we now provide 64-bit windows binary exe files.
An experimental version using 64-bit int is in LIBSVM tools. It in theory can handle up to 2^64 instances/features if memory is enough.
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||1.92||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|
|Python||Python wrapper of LIBLINEAR||Uwe Schmitt||1.32||Zip/tar.gz file|
|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||1.93||perl module|
|Weka||Weka wrapper||Benedikt Waldvogel||1.5||Weka LIBLINEAR|
|R||R interface to LIBLINEAR||Thibault Helleputte||1.8||R LIBLINEAR|
|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|