[liblinear] 16/123: Compact package descriptions as per Soeren's suggestion

[Christian Kastner]

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chrisk-guest pushed a commit to branch master
in repository liblinear.

commit 5230ad0c1518fa8852a6601f9d6450781114ff2c
Author: Christian Kastner <debian at kvr.at>
Date:   Fri Jun 18 12:55:57 2010 +0200

    Compact package descriptions as per Soeren's suggestion
---
 debian/control | 69 +++++++++++++++-------------------------------------------
 1 file changed, 18 insertions(+), 51 deletions(-)

diff --git a/debian/control b/debian/control
index 574d98d..fbbf268 100644
--- a/debian/control
+++ b/debian/control
@@ -17,23 +17,12 @@ Depends:
     liblinear1 (= ${binary:Version}),
     libblas-dev
 Description: Development libraries and header files for LIBLINEAR
- LIBLINEAR is a linear classifier for data with millions of instances and
- features. It supports
- .
-  * L2-regularized classifiers
-    L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
-  * L1-regularized classifiers (after version 1.4)
-    L2-loss linear SVM and logistic regression (LR)
- .
- Main features of LIBLINEAR include
- .
-   * Same data format as LIBSVM
-   * similar usage to LIBSVM
-   * Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
-   * Cross validation for model selection
-   * Probability estimates (logistic regression only)
-   * Weights for unbalanced data
-   * MATLAB/Octave interface
+ LIBLINEAR is a library for learning linear classifiers for large scale
+ applications. It supports Support Vector Machines (SVM) with L2 and L1
+ loss, logistic regression, multi class classification and also Linear
+ Programming Machines (L1-regularized SVMs). Its computational complexity
+ scales linearly with the number of training examples making it one of
+ the fastest SVM solvers around.
  .
  This package contains the header files and static libraries.
 
@@ -48,23 +37,12 @@ Recommends:
 Suggests:
     liblinear-dev (= ${binary:Version})
 Description: Library for Large Linear Classification
- LIBLINEAR is a linear classifier for data with millions of instances and
- features. It supports
- .
-  * L2-regularized classifiers
-    L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
-  * L1-regularized classifiers (after version 1.4)
-    L2-loss linear SVM and logistic regression (LR)
- .
- Main features of LIBLINEAR include
- .
-   * Same data format as LIBSVM
-   * similar usage to LIBSVM
-   * Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
-   * Cross validation for model selection
-   * Probability estimates (logistic regression only)
-   * Weights for unbalanced data
-   * MATLAB/Octave interface
+ LIBLINEAR is a library for learning linear classifiers for large scale
+ applications. It supports Support Vector Machines (SVM) with L2 and L1
+ loss, logistic regression, multi class classification and also Linear
+ Programming Machines (L1-regularized SVMs). Its computational complexity
+ scales linearly with the number of training examples making it one of
+ the fastest SVM solvers around.
  .
  This package contains the shared libraries.
 
@@ -78,22 +56,11 @@ Depends:
 Recommends:
     libsvm-tools
 Description: Library for Large Linear Classification
- LIBLINEAR is a linear classifier for data with millions of instances and
- features. It supports
- .
-  * L2-regularized classifiers
-    L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
-  * L1-regularized classifiers (after version 1.4)
-    L2-loss linear SVM and logistic regression (LR)
- .
- Main features of LIBLINEAR include
- .
-   * Same data format as LIBSVM
-   * similar usage to LIBSVM
-   * Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
-   * Cross validation for model selection
-   * Probability estimates (logistic regression only)
-   * Weights for unbalanced data
-   * MATLAB/Octave interface
+ LIBLINEAR is a library for learning linear classifiers for large scale
+ applications. It supports Support Vector Machines (SVM) with L2 and L1
+ loss, logistic regression, multi class classification and also Linear
+ Programming Machines (L1-regularized SVMs). Its computational complexity
+ scales linearly with the number of training examples making it one of
+ the fastest SVM solvers around.
  .
  This package contains the standalone applications.

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