Parents (Total: 26)

The searched file hash is included in 26 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	1537158
MD5	C199C94E43ABCEA25ABAC66B31CB5AEE
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3
SHA-1	109CF96F11881417AA73B3A0849648C1FD015B86
SHA-256	A74C47E4268DCE64130F1A61D4CCF1A0A203B6C1607B285097817A82480B08AF

Key	Value
FileSize	1538796
MD5	DB4F84D080A258EC2FAA13226C29C70D
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3
SHA-1	13532D4CD2984E2A32D52C95B5D240AE6F4C588C
SHA-256	400CC7EAD2768D0DF18314D45E82F5E0EC73C149A1072EDA2298CC8E4AC89120

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
FileSize	609770
MD5	3C85B3541B258A22A6953C63DAC396F1
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3build4
SHA-1	1F22CBFA9FB2508E4CF3BBE49A11267B0C184486
SHA-256	5A1968EEE5F7C8788E25141FC65FDEE43D49409392F566DA715130CC14E7DE0F

Key	Value
FileSize	1531732
MD5	06695F9BF4BCB387C47CCDC745E194D2
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-8+b1
SHA-1	220EEBC461C6624F4E1BBE39BA99C4C3112E3392
SHA-256	6170315C72691DC3E5751A8A7F04DD60C79AB199DC920EA40DE0904430BB76A4

Key	Value
FileSize	609706
MD5	DC3A319BD94E5047523FA1D1C0D507B6
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3build4
SHA-1	2B83E9427EA31FF528F08E7F5D683C969C86F78A
SHA-256	0A9D167E8F1D35E52703EDA126F8E3F3B31D0CAB1F59C11C60CA6ADA3A329541

Key	Value
FileSize	601324
MD5	AEE06AAC13E2D24DC425C33A8451FBFD
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.1.1-1
SHA-1	3A36E288B18BDB7471294DA5A2699DF16BA80479
SHA-256	EAA74804D3D7E8E77A8B3D6BA731340D2E36066EEE3EFEE0110833C7DBF89ED2

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
FileSize	1531668
MD5	F6D1F2292F4F66D6ADD59B16654AEBAE
PackageDescription	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 package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-8+b1
SHA-1	4B54F26CA2B67B805DB03E2B378EBA5C9A2E1CD7
SHA-256	B813B99FF70FBA8DCD6AAA395C93AD297C8DAFFE8D908C6EB2840945CE535F3A

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