Result for 06243F1DFAA89D73C85D5C1F03BE7E9CEAC42D7E

Query result

Key Value
FileName./usr/share/doc/python-shogun/examples/python_modular/serialization_string_kernels_modular.py.gz
FileSize1752
MD52BEF50BB844954C75A2F2B4B3D8D18A4
SHA-106243F1DFAA89D73C85D5C1F03BE7E9CEAC42D7E
SHA-25665749EB10AC05D95F7E532AEE46604C4E66AA6EF1E4E845207026CC799FC04CB
SSDEEP48:X5p4fNr8w62H4YOHxOni4fNP4FTUhxZ3L/8vY74YXjATWJ1n:J6NI12HROHxceGhxtUv44YcY1n
TLSHT165310B7845E70AA5AD41B2BD955A92E18024F0F3F7663DC77E600C340EC030EA4742BE
hashlookup:parent-total15
hashlookup:trust100

Network graph view

Parents (Total: 15)

The searched file hash is included in 15 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize2563344
MD5F4FA950E6FECE56A9E19E1A4C9EB6E6A
PackageDescriptionLarge 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-10E0591F1E7D5CD0E2FF810FA2E59E4B91CF765BD
SHA-256D10D0FF8BF6E5AD78647C95DE97C3DF535D9B890167CDB5223E3408E72D323F3
Key Value
FileSize3109708
MD5E7000B55376AD3241135FD947E6D9FE4
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-145156560C9F042CD03A613195572BCFEBBD00F6F
SHA-256666A00D2FC804252A6933CE53EC1DAE177ADAE4C644670450F38A66C8178729F
Key Value
FileSize2447164
MD52882063B9B00409EE12BDBD1C87AEB49
PackageDescriptionLarge 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2build2
SHA-19433EFE9646236116111ADD9A9601D4D71FBDFEC
SHA-256904F01FE07BFBF24FC39F5B79BF06D6CF2EBAF73F328262B063E3542DFDE24E3
Key Value
FileSize3108572
MD527B4CE7086DEA1557BBC255FEA6DA2DB
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-4
SHA-196DA2072A1DC5787FFA030C0222D6408DB769E52
SHA-256DB44E3A34137AF56D276F1192D58925D84A0FBBD06BD7FC1E832D71FF674771C
Key Value
FileSize2561618
MD5C1354629C8E463623CD17F448B1251F7
PackageDescriptionLarge 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2build2
SHA-13D641A7DC80551DDEDB12097C87E06F85E52290A
SHA-25671B668FB034B36306545EF0590A74AC1A6CAF2A4BC28429417BF0D19B81CA21C
Key Value
FileSize3308066
MD5716BC53E4BCF1C5180FA98BA7D883DA8
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-4
SHA-1E3B54A50241D4664CAB26C128267A24F06F453F2
SHA-256AF53A196A81BAC8C0AD39EC14CFE0DC1A3AEF7979123FEAFDD0A1A9E47E49B85
Key Value
FileSize3461062
MD54CBD5F15D6C34383AF785A2C99ED8B2E
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-1A5ABE45A8C373B4DDECBFF083E792ECB04B320C2
SHA-25648271F64F5A3A415E20AA05052ABD126B9EDC32F9CC0DB8580760FC6CFBC701F
Key Value
FileSize3255230
MD5F632FE9F948852B20EC1A2417D475804
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-154F6244044B0A6F8ABBD077F9AE4A48341B3A0F0
SHA-2566EFC84F00120ABE3F173FE89CF9689B370B1EF88E974521D203C738CF730C588
Key Value
FileSize3310786
MD555046B3D51A9F314FB217BDDC846E78E
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-15DA43AA0325654F9C5DD5EE18E6E9B785295FC90
SHA-256DEA111356F3E19973277742FE0A4E7CFB447B5726BC30A2C9E18E1D7A43F9192
Key Value
FileSize3126282
MD5573881D086CD9DC751C1512BCC2A4F93
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-1A9BA014E34044877D472C2C95BE1E2F7E05A0B4E
SHA-25631ACB0AC94A3E5B3152BFDC00C8FAAB80BACA2B586E7AEF8709F1E66A8E32ECC
Key Value
FileSize3466904
MD59C32872908144320642D566FBA9359F3
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-4
SHA-12073B038240200F87E8FC1F4553141E9359D7694
SHA-256A6B4AA9D554BAAD2BEA3EDB846AECB63DE74CE922EAB41183CEE2C1D325C6630
Key Value
FileSize3126752
MD52EBB27AD9D216CF03FBB66689891C775
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-4
SHA-1C6D1AFAEE8448E226E3B0E4B89ACAC42D293BBAA
SHA-2562D7D4A4B48A3B3535C8F096A541346E10A9E220546D9AB2CF3709EC0AB4B4889
Key Value
FileSize3074246
MD5ED765C8A718C38B7BBE8053141184594
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-10651CB1BDA9DAA880564F978F2BC68DAA8A4444C
SHA-256D61882A503776554D5972020C3EBD409BA8E2D5DA266042E39ECA93998EF9244
Key Value
FileSize3376006
MD50F7D615D3B8542128FD3F7761ACB37CB
PackageDescriptionLarge 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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-1F3E7F504BEA2FF22106E161A30ED49AC887A7670
SHA-256D2AA4DAE0F8A9A6E4E1F71D797CCA54F8329A615DA0F7E8FBC4FCDA7BE5BED9B
Key Value
FileSize2408936
MD55543E3C427DE9548E2F73AE72274A521
PackageDescriptionLarge 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-shogun
PackageSectionpython
PackageVersion3.2.0-5.2
SHA-1E910FA9937D615EC5C126A557466AB8799E0473C
SHA-256D14ABEF76D1B1431852F4E96AA9A9F912482E10AB4B60E34C2940E718E7BFFF6