1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
Source: liblinear
Priority: optional
Maintainer: Christian Kastner <debian@kvr.at>
Uploaders: Chen-Tse Tsai <ctse.tsai@gmail.com>
Build-Depends:
debhelper (>= 9),
python-all-dev (>= 2.6.6-3~),
libblas-dev
Standards-Version: 3.9.5
X-Python-Version: >= 2.5
XS-Testsuite: autopkgtest
Section: libs
Homepage: http://www.csie.ntu.edu.tw/~cjlin/liblinear/
Vcs-Git: git://anonscm.debian.org/debian-science/packages/liblinear.git
Vcs-Browser: http://anonscm.debian.org/gitweb/?p=debian-science/packages/liblinear.git
Package: liblinear-dev
Section: libdevel
Architecture: any
Multi-Arch: same
Depends:
${misc:Depends},
liblinear1 (= ${binary:Version}),
libblas-dev | libblas.so
Description: Development libraries and header files for LIBLINEAR
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.
Package: liblinear1
Architecture: any
Multi-Arch: same
Pre-Depends:
${misc:Pre-Depends}
Depends:
${shlibs:Depends},
${misc:Depends}
Suggests:
liblinear-tools (= ${binary:Version}),
liblinear-dev (= ${binary:Version})
Description: Library for Large Linear Classification
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. It also provides Python bindings.
.
This package contains the shared libraries.
Package: liblinear-dbg
Priority: extra
Section: debug
Architecture: any
Multi-Arch: same
Depends:
${misc:Depends},
liblinear1 (= ${binary:Version}),
liblinear-tools (= ${binary:Version})
Description: Debugging symbols for LIBLINEAR
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. It also provides Python bindings.
.
This package contains the debugging symbols.
Package: liblinear-tools
Section: science
Architecture: any
Multi-Arch: foreign
Depends:
${shlibs:Depends},
${misc:Depends},
liblinear1 (= ${binary:Version})
Suggests:
libsvm-tools
Description: Standalone applications for LIBLINEAR
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. It also provides Python bindings.
.
This package contains the standalone applications.
Package: python-liblinear
Section: python
Architecture: all
Depends:
${python:Depends},
${misc:Depends},
liblinear1 (>= ${binary:Version}),
liblinear1 (<< ${binary:Version}.1~)
Description: Python bindings for LIBLINEAR
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. It also provides Python bindings.
.
This package contains the Python bindings.
|
