LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM.
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 selection
* Probability estimates (logistic regression only)
* Weights for unbalanced data
* MATLAB/Octave, Java interfaces
L. Hettinger, A. Zehe, A. Dallmann, and A. Hotho. INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, page 191-204. Bonn, Gesellschaft für Informatik e.V., (2019)
F. Otto, M. Ring, D. Landes, and A. Hotho. ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security, page 437. Academic Conferences and publishing limited, (2016)
X. Zhang, and Y. LeCun. (2015)cite arxiv:1502.01710Comment: This technical report is superseded by a paper entitled "Character-level Convolutional Networks for Text Classification", arXiv:1509.01626. It has considerably more experimental results and a rewritten introduction.
S. Aya, C. Lagoze, and T. Joachims. Proceedings of the International Conference on Knowledge Management, page 287--298. World Scientific Publishing, (October 2005)
A. Zubiaga, C. Körner, and M. Strohmaier. Proceedings of the 22nd ACM conference on Hypertext and hypermedia, page 93--102. New York, NY, USA, ACM, (2011)