Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	68277CCE9FF7ABC02535FF7B1EF7DDA4
PackageArch	aarch64
PackageDescription	This package contains the documentation files for shogun in Chinese language. 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.
PackageMaintainer	Fedora Project
PackageName	shogun-doc-cn
PackageRelease	2.fc24
PackageVersion	4.1.0
SHA-1	0C722C9D509FB5995E7AE6A11D3898D9B4860106
SHA-256	0B701828487E708F71CC4D7BC5BD5ED345229AD2E9F59AC11BF5016B539F4CD7

Key	Value
MD5	0C4979D4F74741B3776FB2EE58BBDD8F
PackageArch	aarch64
PackageDescription	This package contains very detailed documentation, and some great examples for shogun. If you need the Chinese API-docs, you would want to install shogun-doc-cn, too. 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.
PackageMaintainer	Fedora Project
PackageName	shogun-doc
PackageRelease	2.fc24
PackageVersion	4.1.0
SHA-1	B19E3540BFB0874DF32A5AF8145E401CA499F7AF
SHA-256	BB0495BEE2B8B9D8D92927920A7DA9B39A42F49760BB1472EBA3C68C16C00E0F