LIBLINEAR -- A Library for Large Linear Classification

Machine Learning Group at National Taiwan University
Contributors

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 LIBLINEAR paper.

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.

Introduction

LIBLINEAR is a linear classifier for data with millions of instances and features. It supports

• L2-regularized classifiers
  L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
• L1-regularized classifiers (after version 1.4)
  L2-loss linear SVM and logistic regression (LR)
• L2-regularized support vector regression (after version 1.9)
  L2-loss linear SVR and L1-loss linear SVR.

Main features of LIBLINEAR include

• Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
• Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
• Cross validation for model evaulation
• Automatic parameter selection
• Probability estimates (logistic regression only)
• Weights for unbalanced data
• MATLAB/Octave, Java, Python, Ruby interfaces

FAQ is here

When to use LIBLINEAR but not LIBSVM

% 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%
2.944s

Warning:If you are a beginner and your data sets are not large, you should consider LIBSVM first.

Download LIBLINEAR

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.

Documentation and Codes used for experiments in our papers

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.

Interfaces to LIBLINEAR