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
F. Karimi, C. Wagner, F. Lemmerich, M. Jadidi, and M. Strohmaier. Proceedings of the 25th International Conference Companion on World Wide Web, page 53--54. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2016)
C. Henning, and R. Ewerth. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, page 14--22. New York, NY, USA, ACM, (2017)
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 (2015)cite arxiv:1502.01710Comment: This technical report is superseded by a paper entitled "Character-level Convolutional Networks for Text Classification", arXiv:1509.01626.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.
A. Hadgu, N. Lotze, and R. Jäschke. Proceedings of the Workshop on Natural Language Processing and Computational Social Science, Hannover, Germany, (May 2016)
E. Boese, and A. Howe. Proceedings of the 14th ACM International Conference on Information and Knowledge Management, page 632--639. New York, NY, USA, ACM, (2005)
D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft. Proceedings of the Third International Conference on Social Computing (SocialCom) and the Third International Conference on Privacy, Security, Risk and Trust (PASSAT), page 180--185. IEEE, (October 2011)
P. Yin, N. Ram, W. Lee, C. Tucker, S. Khandelwal, and M. Salathé. Advances in Knowledge Discovery and Data Mining, volume 8443 of Lecture Notes in Computer Science, Springer International Publishing, (2014)