Rong-En Fan
Chih-Jen Lin
Abstract
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library calls for users and developers. Comprehensive documents are available for both beginners and advanced users. Experiments demonstrate that LIBLINEAR is very efficient on large sparse data sets.
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Digital Library
- C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Google ScholarDigital Library
- C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear SVM. In ICML, 2008. Google ScholarDigital Library
- T. Joachims. Training linear SVMs in linear time. In ACM KDD, 2006. Google ScholarDigital Library
- S. S. Keerthi, S. Sundararajan, K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. A sequential dual method for large scale multi-class linear SVMs. In ACM KDD, 2008. Google ScholarDigital Library
- C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large-scale logistic regression. JMLR, 9:627-650, 2008. Google ScholarDigital Library
- S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: primal estimated sub-gradient solver for SVM. In ICML, 2007. Google ScholarDigital Library
Index Terms
- LIBLINEAR: A Library for Large Linear Classification
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