The current release (Version 2.50, December 2025) of LIBLINEAR can be obtained by downloading the zip file or tar.gz file. You can also check this github directory.

LIBLINEAR can be applied to general problems, but it is particularly useful for document data. We discuss how to transform documents to the input format of LIBLINEAR.

For some multi-class data, the difference between LIBSVM and LIBLINEAR may be significant. The reason is that LIBSVM uses the 1-vs-1 strategy, while LIBLINEAR uses 1-vs-the rest.

You can directly use LIBLINEAR for efficient L1-regularized classification. Use code here only if you are interested in redoing our experiments. The running time is long because we run each solver to …

The development of distributed LIBLINEAR is still in its early stage. Your comments are very welcome.

This is an extension of LIBLINEAR for data which cannot fit in memory. Currently it supports L2-regularized L1- and L2-loss linear SVM, L2-regularized logistic regression, and Cramer and Singer …

In this guide, we provide a step-by-step tutorial of setting up an environment for MPI LIBLINEAR on a cloud computing platform, Amazon Elastic Compute Cloud (EC2).

s not particularly good for this type of problems. Fortunately, we have another software LIBLINEAR (Fan e al., 2008), which is very suitable for such data. We illustrate the di erence between LIBSVM and …

In multi-core LIBLINEAR we implement our own random number generator due to the thread-safety requirement for corss validation. However, from the way MATLAB manages the memory, you may …

LIBLINEAR: a library for large linear classification. It is very suitable for document classification. Version 1.0 released in April 2007. Current Version: 2.50, December 2025. sparsekmeans: efficient kmeans …