Package: shogun-cmdline-static
Source: shogun (3.2.0-8)
Version: 3.2.0-8+b1
Architecture: i386
Maintainer: Debian QA Group <packages@qa.debian.org>
Installed-Size: 1080
Depends: libarpack2 (>= 2.1), libatlas3-base, libbz2-1.0, libc6 (>= 2.4), libcolpack0v5, libcurl3-gnutls (>= 7.16.2), libgcc1 (>= 1:3.0), libglpk40 (>= 4.59), libgomp1 (>= 4.2.1), libhdf5-103, libjson-c3 (>= 0.10), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libnlopt0 (>= 2.2.4), libprotobuf17, libshogun16 (= 3.2.0-8+b1), libsnappy1v5, libstdc++6 (>= 4.1.1), libsz2, libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4)
Conflicts: shogun-cmdline
Replaces: shogun-cmdline
Section: science
Priority: optional
Homepage: http://www.shogun-toolbox.org
Description: Large Scale Machine Learning Toolbox
 SHOGUN - is a new machine learning toolbox with focus on large scale kernel
 methods and especially on Support Vector Machines (SVM) with focus to
 bioinformatics. It provides a generic SVM object interfacing to several
 different SVM implementations. Each of the SVMs can be combined with a variety
 of the many kernels implemented. It can deal with weighted linear combination
 of a number of sub-kernels, each of which not necessarily working on the same
 domain, where  an optimal sub-kernel weighting can be learned using Multiple
 Kernel Learning.  Apart from SVM 2-class classification and regression
 problems, a number of linear methods like Linear Discriminant Analysis (LDA),
 Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to
 train hidden markov models are implemented. 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 comes in different flavours, a stand-a-lone version and also with
 interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Readline
 package.
