besser82 pushed to shogun (master). "updated %%description"

[notifications at fedoraproject.org]

From 3fb820ba0bb7a4dea47d676cbed4d4a840003bba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Bj=C3=B6rn=20Esser?= <bjoern.esser at gmail.com>
Date: Mon, 1 Jun 2015 14:13:49 +0200
Subject: updated %%description


diff --git a/shogun.spec b/shogun.spec
index 8b7c46c..7186ea5 100644
--- a/shogun.spec
+++ b/shogun.spec
@@ -113,54 +113,74 @@
 # The description commonly used by all (sub-)packages.
 %if !%{with svmlight}
 %global common_description							\
-The SHOGUN machine learning toolbox's focus is on large scale kernel methods	\
-and especially on Support Vector Machines (SVM).  It provides a generic SVM	\
-object interfacing to several different SVM implementations, among them the	\
-state of the art LibSVM.  Each of the SVMs can be combined with a variety of	\
-kernels.  The toolbox not only provides efficient implementations of the most	\
-common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but	\
-also comes with a number of recent string kernels as e.g. the Locality		\
-Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts).  For	\
-the latter the efficient LINADD optimizations are implemented.  Also SHOGUN	\
-offers the freedom of working with custom pre-computed kernels.  One of its	\
-key features is the "combined kernel" which can be constructed by a weighted	\
-linear combination of a number of sub-kernels, each of which not necessarily	\
-working on the same domain.  An optimal sub-kernel weighting can be learned	\
-using Multiple Kernel Learning.  Currently SVM 2-class classification and	\
-regression problems can be dealt with.  However SHOGUN also implements a	\
-number of linear methods like Linear Discriminant Analysis (LDA), Linear	\
-Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to	\
-train hidden Markov-models.  The input feature-objects can be dense, sparse	\
-or strings and of type int/short/double/char and can be converted into		\
-different feature types.  Chains of "pre-processors" (e.g. subtracting the	\
-mean) can be attached to each feature object allowing for on-the-fly		\
-pre-processing.									\
+Shogun's focus is on large scale kernel methods and especially on Support	\
+Vector Machines (SVM).  It provides a generic SVM object interfacing to		\
+several different SVM implementations, among them the state of the art OCAS,	\
+Liblinear, LibSVM, SVMLin and GPDT.						\
+										\
+Each of the SVMs can be combined with a variety of kernels.  The toolbox not	\
+only provides efficient implementations of the most common kernels, like the	\
+Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number	\
+of recent string kernels as e.g. the Locality Improved, Fischer, TOP,		\
+Spectrum, Weighted Degree Kernel (with shifts).  For the latter the efficient	\
+LINADD optimizations are implemented.  For linear SVMs the COFFIN framework	\
+allows for on-demand computing feature spaces on-the-fly, even allowing to	\
+mix sparse, dense and other data types.						\
+										\
+Furthermore, SHOGUN offers the freedom of working with custom pre-computed	\
+kernels.  One of its key features is the combined kernel which can be		\
+constructed by a weighted linear combination of a number of sub-kernels,	\
+each of which not necessarily working on the same domain.  An optimal		\
+sub-kernel weighting can be learned using Multiple Kernel Learning.		\
+Currently SVM one-class, 2-class and multiclass classification and		\
+regression problems can be dealt with.						\
+										\
+SHOGUN also implements a number of linear methods like Linear Discriminant	\
+Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and	\
+features algorithms to train hidden markov models.  The input feature-objects	\
+can be dense, sparse or strings and of type int/short/double/char and can be	\
+converted into different feature types.  Chains of preprocessors (e.g.		\
+substracting the mean) can be attached to each feature object allowing for	\
+on-the-fly pre-processing.							\
+										\
+SHOGUN is implemented in C++ with interfaces to R, Octave and Python and is	\
+proudly released as Machine Learning Open Source Software.			\
 										\
 This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because	\
 of it's 'no-redistribute', 'no-commercial-use' license.
 %else  # !%%{with svmlight}
-The SHOGUN machine learning toolbox's focus is on large scale kernel methods	\
-and especially on Support Vector Machines (SVM).  It provides a generic SVM	\
-object interfacing to several different SVM implementations, among them the	\
-state of the art LibSVM.  Each of the SVMs can be combined with a variety of	\
-kernels.  The toolbox not only provides efficient implementations of the most	\
-common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but	\
-also comes with a number of recent string kernels as e.g. the Locality		\
-Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts).  For	\
-the latter the efficient LINADD optimizations are implemented.  Also SHOGUN	\
-offers the freedom of working with custom pre-computed kernels.  One of its	\
-key features is the "combined kernel" which can be constructed by a weighted	\
-linear combination of a number of sub-kernels, each of which not necessarily	\
-working on the same domain.  An optimal sub-kernel weighting can be learned	\
-using Multiple Kernel Learning.  Currently SVM 2-class classification and	\
-regression problems can be dealt with.  However SHOGUN also implements a	\
-number of linear methods like Linear Discriminant Analysis (LDA), Linear	\
-Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to	\
-train hidden Markov-models.  The input feature-objects can be dense, sparse	\
-or strings and of type int/short/double/char and can be converted into		\
-different feature types.  Chains of "pre-processors" (e.g. subtracting the	\
-mean) can be attached to each feature object allowing for on-the-fly		\
-pre-processing.
+Shogun's focus is on large scale kernel methods and especially on Support	\
+Vector Machines (SVM).  It provides a generic SVM object interfacing to		\
+several different SVM implementations, among them the state of the art OCAS,	\
+Liblinear, LibSVM, SVMLight, SVMLin and GPDT.					\
+										\
+Each of the SVMs can be combined with a variety of kernels.  The toolbox not	\
+only provides efficient implementations of the most common kernels, like the	\
+Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number	\
+of recent string kernels as e.g. the Locality Improved, Fischer, TOP,		\
+Spectrum, Weighted Degree Kernel (with shifts).  For the latter the efficient	\
+LINADD optimizations are implemented.  For linear SVMs the COFFIN framework	\
+allows for on-demand computing feature spaces on-the-fly, even allowing to	\
+mix sparse, dense and other data types.						\
+										\
+Furthermore, SHOGUN offers the freedom of working with custom pre-computed	\
+kernels.  One of its key features is the combined kernel which can be		\
+constructed by a weighted linear combination of a number of sub-kernels,	\
+each of which not necessarily working on the same domain.  An optimal		\
+sub-kernel weighting can be learned using Multiple Kernel Learning.		\
+Currently SVM one-class, 2-class and multiclass classification and		\
+regression problems can be dealt with.						\
+										\
+SHOGUN also implements a number of linear methods like Linear Discriminant	\
+Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and	\
+features algorithms to train hidden markov models.  The input feature-objects	\
+can be dense, sparse or strings and of type int/short/double/char and can be	\
+converted into different feature types.  Chains of preprocessors (e.g.		\
+substracting the mean) can be attached to each feature object allowing for	\
+on-the-fly pre-processing.							\
+										\
+SHOGUN is implemented in C++ with interfaces to R, Octave and Python and is	\
+proudly released as Machine Learning Open Source Software.
 %endif # !%%{with svmlight}
 
 ###############################################################################
-- 
cgit v0.10.2


	http://pkgs.fedoraproject.org/cgit/shogun.git/commit/?h=master&id=3fb820ba0bb7a4dea47d676cbed4d4a840003bba