liblinear1.93
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
- L2-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
文件下载
资源详情
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