Parents (Total: 6)

The searched file hash is included in 6 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	B30C8039D4033C6F6BE28892A49A7AB0
PackageArch	aarch64
PackageDescription	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. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains files needed for development with shogun.
PackageMaintainer	Fedora Project
PackageName	shogun-devel
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	83990CB9290EE907E1997570DA7CFDAF6B5652BC
SHA-256	AEC88E47814BCE5B963F69BAEDF2394BED4D90DBCB60D32AF29950C575BA9E1A

Key	Value
MD5	2D9D90212A7E76BD4EE4CE9E9856E35D
PackageArch	aarch64
PackageDescription	This package provides debug information for package shogun. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	shogun-debuginfo
PackageRelease	2.fc24
PackageVersion	4.1.0
SHA-1	B47191F6F30FB228C311A2BB21369CEA150780A4
SHA-256	F9FE3BA473ED1891FD68E4B6B821BC6265615B067601C7F335AB4CD8E20F4232

Key	Value
MD5	49B1198BB411D8B931D83CAD05C52953
PackageArch	aarch64
PackageDescription	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.
PackageMaintainer	Fedora Project
PackageName	shogun-devel
PackageRelease	2.fc24
PackageVersion	4.1.0
SHA-1	1EF3941248398152C3802F76FFD76756F7D687F9
SHA-256	5174AB631D7B2DB302FCF21915BD69C9C231C47F89E878F25F934B7EDED4DDBB

Key	Value
MD5	0D6B306C987FA60D7F794160AD5A2A88
PackageArch	aarch64
PackageDescription	This package provides debug information for package shogun. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	shogun-debuginfo
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	721FE4E89FBAA86E4B726FD2ED472E50BACFCBE2
SHA-256	6FB7EEE00767CEE7C2BFB61843F223B400D1B6A51A523BA758E2D85F21CA4A1C

Key	Value
MD5	D1E77DE63D2634FE76795D53AC3B2F53
PackageArch	aarch64
PackageDescription	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. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains files needed for development with shogun.
PackageMaintainer	Fedora Project
PackageName	shogun-devel
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	434DDF67E4BD63CDE78F8A8A5C10E048BA1BB837
SHA-256	5E5448650A1905BF049BFDFD859411FC93A2CEEFA8605287F6097F7FA4F7D645

Key	Value
MD5	7F957E87B09F955F287DF7D06979AD86
PackageArch	aarch64
PackageDescription	This package provides debug information for package shogun. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	shogun-debuginfo
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	72E2473684B1E16D0F6C2D1FA6497CBBF08C7E23
SHA-256	B6E8F56C27E5D1E924088809CBFBC221AAD34280ED9CEB2322038C1FDD391D5E