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| author | Rong-En Fan <rafan@FreeBSD.org> | 2009-10-29 11:09:53 +0000 |
|---|---|---|
| committer | Rong-En Fan <rafan@FreeBSD.org> | 2009-10-29 11:09:53 +0000 |
| commit | feb5c398589cb3992e278ca2cab5ba8e1f6cba73 (patch) | |
| tree | b741a33b2f37749c6c628c3b583217cd8151b0cb /science | |
| parent | e05f23caea1414d7534cd61eaddec0fd48429af3 (diff) | |
| download | ports-feb5c398589cb3992e278ca2cab5ba8e1f6cba73.tar.gz ports-feb5c398589cb3992e278ca2cab5ba8e1f6cba73.zip | |
Notes
Diffstat (limited to 'science')
| -rw-r--r-- | science/liblinear/Makefile | 2 | ||||
| -rw-r--r-- | science/liblinear/distinfo | 6 | ||||
| -rw-r--r-- | science/liblinear/pkg-descr | 7 |
3 files changed, 8 insertions, 7 deletions
diff --git a/science/liblinear/Makefile b/science/liblinear/Makefile index cce750d3ce3d..fda5ccdb8fb1 100644 --- a/science/liblinear/Makefile +++ b/science/liblinear/Makefile @@ -6,7 +6,7 @@ # PORTNAME= liblinear -PORTVERSION= 1.34 +PORTVERSION= 1.50 CATEGORIES= science math MASTER_SITES= http://www.csie.ntu.edu.tw/~cjlin/liblinear/ \ http://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/ diff --git a/science/liblinear/distinfo b/science/liblinear/distinfo index db014d8c84af..d9e438928971 100644 --- a/science/liblinear/distinfo +++ b/science/liblinear/distinfo @@ -1,3 +1,3 @@ -MD5 (liblinear-1.34.zip) = 788cd7d7b2500c0ccfdf75bab95ff656 -SHA256 (liblinear-1.34.zip) = 1f25b1ec3d021f6ac387bcd58c2ac5e536f52fe595ac45587f17da612affb08f -SIZE (liblinear-1.34.zip) = 207305 +MD5 (liblinear-1.5.zip) = 47024e6ff826ad044e5c9e264f9893c4 +SHA256 (liblinear-1.5.zip) = 8167364e225426de81dc009868950bdaa5af4f868c02ba89f80b737efdeff507 +SIZE (liblinear-1.5.zip) = 215752 diff --git a/science/liblinear/pkg-descr b/science/liblinear/pkg-descr index b0dc747b819b..14581ea99123 100644 --- a/science/liblinear/pkg-descr +++ b/science/liblinear/pkg-descr @@ -1,11 +1,12 @@ 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. +features. It supports L2-regularized classifiers (L2-loss linear SVM, +L1-loss linear SVM, and logistic regression), L1-regularized classifiers +(L2-loss linear SVM and logistic regression). Main features of LIBLINEAR include - Same data format as LIBSVM and similar usage -- One-vs-the rest multi-class classification +- One-vs-the rest and Crammer & Singer multi-class classification - Cross validation for model selection - Probability estimates (logistic regression only) - Weights for unbalanced data |