Parents (Total: 21)

The searched file hash is included in 21 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	3074246
MD5	ED765C8A718C38B7BBE8053141184594
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 contains the static and the modular Python interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2
SHA-1	0651CB1BDA9DAA880564F978F2BC68DAA8A4444C
SHA-256	D61882A503776554D5972020C3EBD409BA8E2D5DA266042E39ECA93998EF9244

Key	Value
FileSize	2563344
MD5	F4FA950E6FECE56A9E19E1A4C9EB6E6A
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 contains the static and the modular Python interfaces.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2
SHA-1	0E0591F1E7D5CD0E2FF810FA2E59E4B91CF765BD
SHA-256	D10D0FF8BF6E5AD78647C95DE97C3DF535D9B890167CDB5223E3408E72D323F3

Key	Value
MD5	9AFE7FB781D39DB05A5A5A63D1CB843C
PackageArch	aarch64
PackageDescription	This package contains the Python3-plugin for 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	python3-shogun
PackageRelease	2.fc24
PackageVersion	4.1.0
SHA-1	1E14D77716A6EE4B1FB3E32602ACA5FDD294817C
SHA-256	EE746E59B31204BAA5A20797C1522DF0E8A141B1353E1A82DEEEFC5F55183592

Key	Value
FileSize	3466904
MD5	9C32872908144320642D566FBA9359F3
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 contains the static and the modular Python interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-4
SHA-1	2073B038240200F87E8FC1F4553141E9359D7694
SHA-256	A6B4AA9D554BAAD2BEA3EDB846AECB63DE74CE922EAB41183CEE2C1D325C6630

Key	Value
FileSize	2561618
MD5	C1354629C8E463623CD17F448B1251F7
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 contains the static and the modular Python interfaces.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2build2
SHA-1	3D641A7DC80551DDEDB12097C87E06F85E52290A
SHA-256	71B668FB034B36306545EF0590A74AC1A6CAF2A4BC28429417BF0D19B81CA21C

Key	Value
FileSize	3109708
MD5	E7000B55376AD3241135FD947E6D9FE4
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 contains the static and the modular Python interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2
SHA-1	45156560C9F042CD03A613195572BCFEBBD00F6F
SHA-256	666A00D2FC804252A6933CE53EC1DAE177ADAE4C644670450F38A66C8178729F

Key	Value
FileSize	3255230
MD5	F632FE9F948852B20EC1A2417D475804
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 contains the static and the modular Python interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2
SHA-1	54F6244044B0A6F8ABBD077F9AE4A48341B3A0F0
SHA-256	6EFC84F00120ABE3F173FE89CF9689B370B1EF88E974521D203C738CF730C588

Key	Value
FileSize	3310786
MD5	55046B3D51A9F314FB217BDDC846E78E
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 contains the static and the modular Python interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	python-shogun
PackageSection	python
PackageVersion	3.2.0-5.2
SHA-1	5DA43AA0325654F9C5DD5EE18E6E9B785295FC90
SHA-256	DEA111356F3E19973277742FE0A4E7CFB447B5726BC30A2C9E18E1D7A43F9192

Key	Value
MD5	366F32020F8BACDF2CD383ED94A8C227
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 the Python3-plugin for shogun.
PackageMaintainer	Fedora Project
PackageName	python3-shogun
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	8E3E5FD3B34E9DA012715EBBB08D1A8B3840B554
SHA-256	7521DEDAE5281A1913B3C75D2E17DF4CD8BF3C52227EE19259479484B04A2640

Key	Value
MD5	9DD6429D2095A7C040B95E7D59D6F695
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 the Python-plugin for shogun.
PackageMaintainer	Fedora Project
PackageName	python-shogun
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	904BA589EF26FA8F74661C0B75FF7001CC99D394
SHA-256	F144B5C64461F90BAA5B16B11C678ED7544406C95541951ECC85D186A5FAAAEF