Result for D0CFF3E7EA3A82F488A7ADC62CC1F45FF7B3E126

Query result

Key Value
FileName./usr/share/doc/libshogun16/README.Debian
FileSize556
MD5B21C363C06593A6D784FDE463D50A665
SHA-1D0CFF3E7EA3A82F488A7ADC62CC1F45FF7B3E126
SHA-256D167DB26B0A39E35D54417699BA00D9B665FFBCC94987A535EDDD579A285AF9D
SSDEEP12:hVg/jyckuLBsRCvshZ1SEOFUw9PWqe2uGSPh2+uXH8HbF3kzXB//59JXzl9LM4v:0jJkuLBs4A1SEOFp97ekSPh2+usHheR3
TLSHT15FF0265482113B755C81FE6771B4AEB033D5195C2B3060901C1F49CDA3527D4343B5C5
hashlookup:parent-total10
hashlookup:trust100

Network graph view

Parents (Total: 10)

The searched file hash is included in 10 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize3485632
MD5CEDA436D1EC0D7A80FF2BDCE08866790
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-10526A484348B169E2AEA17BE319CD46885EE3B6F
SHA-256ECA9FC936BBF9C65BF0A8502FCDAA3FA4B0E350510C1646F6DD8C557B3A77496
Key Value
FileSize3856804
MD5D841807F7A3EF76775AFA40DBCF2409E
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-109691E7FAAAEE2671783836D4FC69B7418634EFF
SHA-256D4E34EC0C95F426D587D19915F35A1F467E428097B7EEA401541E817F6C4BE3E
Key Value
FileSize3336668
MD5D0E1513E57D68FA98B0ABEF2E2344782
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-1A5469B8CB1D7B4F6A2D23DF59266136EA65943CA
SHA-2569999CEDB5953535E834DDE8F2EA1C74EE815B6B9E0FE44C43D99FE03577176A3
Key Value
FileSize3534684
MD5F67F03D8B76443CEEC3DD730B454B0EF
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-1FB016F2622472AE0B26C161348FBE053E1F1B840
SHA-25622B641ADA0155254AA6B99640272C80729A029BC9834EFB7CF12B8F7C480DD75
Key Value
FileSize3940372
MD54AF0035AED02444E82FA247A9874FE40
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-16D0988942A2FC259E7D93B22CFF929BBA6E15694
SHA-2563D5AA3776D6787977A5A9E1F093E89B786CEA563B05257512C0AD8FDCA63DF09
Key Value
FileSize3533160
MD5D11120ADC3CDCB713106466AE5E1BAB0
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-1B7871D91DB009AAB9A5BA54AD1924DA8056F4EE4
SHA-2561E05D48398047B41EE9E35B615937FD4C8893E89828D3F0A9A251EA77BF5D8F6
Key Value
FileSize3346556
MD5B036FABAE55BAB35BA90F705FC300AA3
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-15015C99CE3D3A1A11D69913D150B74E3F69BB179
SHA-2564D6641397F04647DED73724EB1DC31C8E689F3339B63A8E0DE40D457FC0AD14C
Key Value
FileSize3915168
MD57A62E7BB638B596CC1301E10CF53BBAB
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-152B9BCCA7B18B0E04FD07E65144090B7499C4850
SHA-256ECC7DAF99C50FF052C9CDD4BD8AE1622FCB9D7FA50C35C97B82A50301FBFEE36
Key Value
FileSize3278436
MD50C877FDFAA39E11F0D12489489435EAB
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-16ACA405D302A7FD418F854D8F5EACA1730C94A46
SHA-256C003B5B5599343888E42BA40D86E25AD54F41BC6D7B583359FC7257E9020D0A8
Key Value
FileSize3527448
MD5285CE3FC84EB22CF517F1FAD4D9780B8
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 is the core library with the machine learning methods and ui helpers all interfaces are based on.
PackageMaintainerDebian QA Group <packages@qa.debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-8+b1
SHA-140628F9ED687A8CBBE380E1B548BED91E19A3DD0
SHA-2561B1364B11344868184FF9B709440ADB495D78875ECC3798E5CA997DFBA93D3B6