| Name : shogun-devel
| |
| Version : 4.1.0
| Vendor : Fedora Project
|
| Release : 2.fc22
| Date : 2016-02-24 17:44:17
|
| Group : Unspecified
| Source RPM : shogun-4.1.0-2.fc22.src.rpm
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| Size : 5.14 MB
| |
| Packager : Fedora Project
| |
| Summary : Development files for shogun
|
Description :
This package contains files needed for development with shogun.
The Shogun Machine learning toolbox provides a wide range of unified and
efficient Machine Learning (ML) methods. The toolbox seamlessly allows to
easily combine multiple data representations, algorithm classes, and general
purpose tools. This enables both rapid prototyping of data pipelines and
extensibility in terms of new algorithms. We combine modern software
architecture in C++ with both efficient low-level computing back-ends and
cutting edge algorithm implementations to solve large-scale Machine Learning
problems (yet) on single machines.
One of Shogun\'s most exciting features is that you can use the toolbox through
a unified interface from C++, Python(3), Octave, R, Java, Lua, etc. This not
just means that we are independent of trends in computing languages, but it
also lets you use Shogun as a vehicle to expose your algorithm to multiple
communities. We use SWIG to enable bidirectional communication between C++
and target languages. Shogun runs under Linux/Unix, MacOS, Windows.
Originally focusing on large-scale kernel methods and bioinformatics (for a
list of scientific papers mentioning Shogun, see here), the toolbox saw
massive extensions to other fields in recent years. It now offers features
that span the whole space of Machine Learning methods, including many
classical methods in classification, regression, dimensionality reduction,
clustering, but also more advanced algorithm classes such as metric,
multi-task, structured output, and online learning, as well as feature
hashing, ensemble methods, and optimization, just to name a few. Shogun in
addition contains a number of exclusive state-of-the art algorithms such as
a wealth of efficient SVM implementations, Multiple Kernel Learning, kernel
hypothesis testing, Krylov methods, etc. All algorithms are supported by a
collection of general purpose methods for evaluation, parameter tuning,
preprocessing, serialization & I/O, etc; the resulting combinatorial
possibilities are huge.
The wealth of ML open-source software allows us to offer bindings to other
sophisticated libraries including: LibSVM, LibLinear, LibOCAS, libqp,
VowpalWabbit, Tapkee, SLEP, GPML and more.
Shogun got initiated in 1999 by Soeren Sonnenburg and Gunnar Raetsch (that\'s
where the name ShoGun originates from). It is now developed by a larger team
of authors, and would not have been possible without the patches and bug
reports by various people. See contributions for a detailed list. Statistics
on Shogun\'s development activity can be found on ohloh.
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RPM found in directory: /packages/linux-pbone/archive.fedoraproject.org/fedora/linux/updates/22/x86_64/s |
