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authorRong-En Fan <rafan@FreeBSD.org>2008-06-03 02:29:39 +0000
committerRong-En Fan <rafan@FreeBSD.org>2008-06-03 02:29:39 +0000
commit686bc0486252cc52f7f99081552d6a0913e02c7f (patch)
tree93d7391893a87101dc7cb4e5899928b4070352ce /science/liblinear
parenta4e8cb83b32769713942944fabdbcaceb2e1fa93 (diff)
downloadports-686bc0486252cc52f7f99081552d6a0913e02c7f.tar.gz
ports-686bc0486252cc52f7f99081552d6a0913e02c7f.zip
- Update pkg-descr for recent updates
Notes
Notes: svn path=/head/; revision=214203
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LIBLINEAR is a linear classifier for data with millions of instances and
-features. It supports both logistic regression and L2-loss linear SVM using a
-trust region Newton method in
-
-C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method
-for large-scale regularized logistic regression. Technical report, 2007.
-A short version appears in ICML 2007.
+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 and similar usage
-One-vs-the rest multi-class classification
-Cross validation for model selection
-Probability estimates (logistic regression only)
-Weights for unbalanced data
+- Same data format as LIBSVM and similar usage
+- One-vs-the rest multi-class classification
+- Cross validation for model selection
+- Probability estimates (logistic regression only)
+- Weights for unbalanced data
WWW: http://www.csie.ntu.edu.tw/~cjlin/liblinear/