Parents (Total: 18)

The searched file hash is included in 18 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	3435276
MD5	55C23AED16C9E6FE0199B69ACC9B681B
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun16
PackageSection	libs
PackageVersion	3.2.0-7.3
SHA-1	0480579773BC6B0E1D692DE6F003739FE9EB22E1
SHA-256	485EFA1F754527841C3611BA0831314A494944A122BC24DA48BED8CF127CA8FC

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
FileSize	46244432
MD5	CD9278273EEA71968E9D1D3950778E2F
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 debug symbols for all interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dbg
PackageSection	debug
PackageVersion	3.2.0-7.3
SHA-1	27ED79F19414935D70CA84748E149C5BD47D24BD
SHA-256	DEA4137F6C52AEDF7123CE82F8600C22CA7BBC4E94E1BD016A51927604D21728

Key	Value
FileSize	959816
MD5	C0EEA8C7E3C7ABBC77E3D9C2457FC9FA
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 is the Readline package.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-cmdline-static
PackageSection	science
PackageVersion	3.2.0-7.3
SHA-1	2F8E2093CEDB10ACD5E0A8840B71DE1FC0E93C95
SHA-256	A935DC99A9B361146234BE82225EE7C4638577C917CFD0BE9683C5902B0BFA50

Key	Value
FileSize	958188
MD5	72090155AEE69C64632B82EFCF80C5FB
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 is the Readline package.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-cmdline-static
PackageSection	science
PackageVersion	3.2.0-7.3
SHA-1	6C38B74919B9073257149FF4766978CC8EEB2CBF
SHA-256	FA1242E9C6789222FB3ACB1D969A5DAC58042A9DC59EB3A0D828EDE638DC0F40

Key	Value
FileSize	959296
MD5	9DCAA8AE58A6DF3C86A6D35235CBC36C
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 is the Readline package.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-cmdline-static
PackageSection	science
PackageVersion	3.2.0-7.3
SHA-1	76AA8FD6EBAC8A94CCF2527C70B0BD65B5CDC789
SHA-256	51B336068A48A4E06EEBB83AF88E5AFE9F9A23A678BB50147D6AD3418F67B1FD

Key	Value
FileSize	29119232
MD5	F8711E3571D12DFA5B991E056314E994
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 is the English user and developer documentation.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-doc-en
PackageSection	doc
PackageVersion	3.2.0-7.3
SHA-1	7A5D1EFDB2818B39BC6BAE1C30B128999791CB7C
SHA-256	EFED65FCE7B2A82C724AFC078A65AB18CC7445BA035B9313DF20BF31F05A0743

Key	Value
FileSize	44439266
MD5	B4E5C685A7FE9148CBB4A2CA0903BBBB
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 debug symbols for all interfaces.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dbg
PackageSection	debug
PackageVersion	3.2.0-7.3
SHA-1	B0620F6478A87619601E757D18DE154D3A8094DF
SHA-256	1CBBBCEB1B81298DBF6AE7310A65F25B137E49D5E90F3A27A21ED7881BE5BC5A

Key	Value
FileSize	1537088
MD5	6CAAD737E04B825D4B0CF6A07EE52627
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	BA87A38510977769037F3064B2F296CBE0CFF8F2
SHA-256	4B19C07379A444E7B65E765966F3109EF77B7F987977A60734205DAB1E74BC40