[libocas] 13/60: Compact package descriptions as per Soeren's suggestion

[Christian Kastner]

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

commit ee213762aeba49fa5da78f04988c36f6242d5602
Author: Christian Kastner <debian at kvr.at>
Date:   Fri Jun 18 13:03:40 2010 +0200

    Compact package descriptions as per Soeren's suggestion
---
 debian/control | 78 +++++++++++-----------------------------------------------
 1 file changed, 15 insertions(+), 63 deletions(-)

diff --git a/debian/control b/debian/control
index 2ec4a9e..eaaeb94 100644
--- a/debian/control
+++ b/debian/control
@@ -15,27 +15,11 @@ Architecture: any
 Depends:
     libocas0 (= ${binary:Version})
 Description: Development libraries and header files for LIBOCAS
- This library implements Optimized Cutting Plane Algorithm (OCAS) for training
- linear SVM classifiers from large-scale data. The computational effort of OCAS
- scales with O(m log m) where m is the sample size. In an extensive empirical
- evaluation OCAS significantly outperforms current state of the art SVM solvers,
- like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some
- datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise
- Support Vector solution.
- .
-  * SVM solvers for training linear classifiers from large scale-data
-  * Binary (two-class) and genuine multi-class SVM formulations
-  * Optimized code written in C
-  * A stand alone application and MEX interface for Matlab
-  * Reads examples from SVM^light format
-  * Optimized for both sparse and dense features
-  * Parallelized version of the binary solver
-  * Allows using different C for each training example (Matlab's interace to
-  * binary solver)
-  * Tools for classification
-  * Training translation invariant image classifiers from virtual examples
-  * Functions for computing image features based on Local Binary Patterns
-  * (LBP)
+ This library implements Optimized Cutting Plane Algorithm (OCAS) for
+ training linear Support Vector Machine (SVM) classifiers from
+ large-scale data. The computational effort of OCAS scales linearly with
+ the number of training examples. It is one of the fastest SVM solvers
+ around for solving linear and multiclass L2 regularized SVMs.
  .
  This package contains the header files and static libraries.
 
@@ -50,27 +34,11 @@ Recommends:
 Suggests:
     libocas-dev (= ${binary:Version})
 Description: OCAS solver for training linear SVM classifiers
- This library implements Optimized Cutting Plane Algorithm (OCAS) for training
- linear SVM classifiers from large-scale data. The computational effort of OCAS
- scales with O(m log m) where m is the sample size. In an extensive empirical
- evaluation OCAS significantly outperforms current state of the art SVM solvers,
- like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some
- datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise
- Support Vector solution.
- .
-  * SVM solvers for training linear classifiers from large scale-data
-  * Binary (two-class) and genuine multi-class SVM formulations
-  * Optimized code written in C
-  * A stand alone application and MEX interface for Matlab
-  * Reads examples from SVM^light format
-  * Optimized for both sparse and dense features
-  * Parallelized version of the binary solver
-  * Allows using different C for each training example (Matlab's interace to
-  * binary solver)
-  * Tools for classification
-  * Training translation invariant image classifiers from virtual examples
-  * Functions for computing image features based on Local Binary Patterns
-  * (LBP)
+ This library implements Optimized Cutting Plane Algorithm (OCAS) for
+ training linear Support Vector Machine (SVM) classifiers from
+ large-scale data. The computational effort of OCAS scales linearly with
+ the number of training examples. It is one of the fastest SVM solvers
+ around for solving linear and multiclass L2 regularized SVMs.
  .
  This package contains the shared libraries.
 
@@ -82,26 +50,10 @@ Depends:
     ${misc:Depends}
     libocas0 (= ${binary:Version})
 Description: Standalone applications implementing the OCAS solver
- This library implements Optimized Cutting Plane Algorithm (OCAS) for training
- linear SVM classifiers from large-scale data. The computational effort of OCAS
- scales with O(m log m) where m is the sample size. In an extensive empirical
- evaluation OCAS significantly outperforms current state of the art SVM solvers,
- like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some
- datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise
- Support Vector solution.
- .
-  * SVM solvers for training linear classifiers from large scale-data
-  * Binary (two-class) and genuine multi-class SVM formulations
-  * Optimized code written in C
-  * A stand alone application and MEX interface for Matlab
-  * Reads examples from SVM^light format
-  * Optimized for both sparse and dense features
-  * Parallelized version of the binary solver
-  * Allows using different C for each training example (Matlab's interace to
-  * binary solver)
-  * Tools for classification
-  * Training translation invariant image classifiers from virtual examples
-  * Functions for computing image features based on Local Binary Patterns
-  * (LBP)
+ This library implements Optimized Cutting Plane Algorithm (OCAS) for
+ training linear Support Vector Machine (SVM) classifiers from
+ large-scale data. The computational effort of OCAS scales linearly with
+ the number of training examples. It is one of the fastest SVM solvers
+ around for solving linear and multiclass L2 regularized SVMs.
  .
  This package contains the standalone applications.

-- 
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