LIBLINEAR is a simple package for solving large-scale regularized linear classification and regression. It currently supports - L2-regularized logistic regression/L2-loss support vector classification/L1-loss support vector classification - L1-regularized L2-loss support vector classification/L1-regularized logistic regression - L 2-regularized L2-loss support vector regression/L1-loss support vector regression. When to use LIBLINEAR but not LIBSVM ==================================== There are some large data for which with/without nonlinear mappings gives similar performances. Without using kernels, one can efficiently train a much larger set via linear classification/regression. These data usually have a large number of features. Document classification is an example. Warning: While generally liblinear is very fast, its default solver may be slow under certain situations (e.g., data not scaled or C is large). See Appendix B of our SVM guide about how to handle such cases. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf Warning: If you are a beginner and your data sets are not large, you should consider LIBSVM first. LIBSVM page: http://www.csie.ntu.edu.tw/~cjlin/libsvm 2-regularized L2-loss support vector regression/L1-loss support vector regression. When to use LIBLINEAR but not LIBSVM ==================================== There are some large data for which with/without nonlinear mappings gives similar performances. Without using kernels, one can efficiently train a much larger set via linear classification/regression. These data usually have a large number of features. Document classification is an example. Warning: While generally liblinear is very fast, its default solver may be slow under certain situations (e.g., data not scaled or C is large). See Appendix B of our SVM guide about how to handle such cases. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf Warning: If you are a beginner and your data sets are not large, you should consider LIBSVM first. LIBSVM page: http://www.csie.ntu.edu.tw/~cjlin/libsvm