Result for 9A204E7477531E9B1F5108A891294A1D5632DB5A

Query result

Key Value
FileName./usr/share/doc/libshogun16/README.Debian
FileSize555
MD57CADE02397B918099D24B3A7C5A337F1
SHA-19A204E7477531E9B1F5108A891294A1D5632DB5A
SHA-256B06C3C0F0D93E6FF74D134197E251F9258F5959439ED349F31709C126F05D9EC
SSDEEP12:hVg/jyckuLBsRCvshZ1SEOFUw9PWqe2uGSPh2+uXH8HbF3kzXBvN59JXzl9LM4v:0jJkuLBs4A1SEOFp97ekSPh2+usHheRl
TLSHT106F0265482113B755C81FE6771B4AEF033D5195C2B3060901C1F49CDA3517D4343B5C5
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
FileSize2884574
MD5E3E152F2DD875DA10D79EAB4B467350C
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.5
SHA-1EE0262BB32FEF0BFC877084BE61EAAF2205F8260
SHA-256D8355CDA3B0FD811C91CE2F043F94596491292119B36843CDDF162BB63D0B2DC
Key Value
FileSize3463940
MD52F1D8FC08B7D0ECD364AA1C3094E6BFD
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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3
SHA-1D204804637BA183DCEBDFDA9E44B6017F06374F3
SHA-2564A065E80B120EA74E67188879CA36F371774365302D8AA2A3B4286534DEF788B
Key Value
FileSize2548932
MD523185416C02B63833F254DD9D776DDF3
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun15
PackageSectionlibs
PackageVersion3.1.1-1
SHA-1BD3F07220006517F7CC3668657B86DAE7149404A
SHA-256F8F9949044273B0D0038864FA9A301322D56F56B7FAFB7D7C652EC87FC7CBCFE
Key Value
FileSize2747062
MD5EB770003D27DBF58E183D457C0545DF2
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3build4
SHA-16C6257B3749CE629DF507285BBEFDC55FA9AA2C1
SHA-2562960098448B0F57AAEC5C0E328739BF1B6D1D1F145F460C6D09BD8B951EFD79C
Key Value
FileSize3720654
MD549755D1004BC596720C1936881E4C983
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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3
SHA-1D28DBCF9CD99BF198B9F7040DEC11F83EF3063E5
SHA-25636ABD1F8014BB2DF1F5AF36D43A545030EDD2C6ED2BC3190E48B5434826C28D6
Key Value
FileSize2814378
MD5DD3CAB291F019C0E210EF120B50FDB49
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3build4
SHA-1CAC91E5787DD2216566014BD55132D9966AE4EC0
SHA-256B4552DF613D2AB4EE10DCAD9FBAB19AFC9D696A1D41ABB1BD27394D0ED953BBF
Key Value
FileSize3790950
MD51BB0BE9E50FAFB6ECC01B1EFD958A08B
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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3
SHA-1CA90217C66A9D3E14B9C7E76731B5DED9A2844C7
SHA-2561C8A6138037A972D9940C4956DD63829023D6CBD00267CFEB97C2CAA4560463B
Key Value
FileSize2852358
MD58A58A0F101006AFC7A1A149F48E243F1
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.5
SHA-1FEA711EFE414C6A7FCE474A4CBA79BCEBE911A86
SHA-256A6D399148AA37209681F94940FD14D4D5850FC84E4F97501B47819623DC50C31
Key Value
FileSize2421842
MD5EDA966E5F7AA3B4BD5B17FBFA42F40AD
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibshogun15
PackageSectionlibs
PackageVersion3.1.1-1
SHA-1C1CC1AF01EF9D25BCF6E2C20E1FF82D445B5808B
SHA-2560B944C0E62F4E677D3DCB2F79DF9715F9D7A14296D4565ACCCA92023474DF35F
Key Value
FileSize3435276
MD555C23AED16C9E6FE0199B69ACC9B681B
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.
PackageMaintainerSoeren Sonnenburg <sonne@debian.org>
PackageNamelibshogun16
PackageSectionlibs
PackageVersion3.2.0-7.3
SHA-10480579773BC6B0E1D692DE6F003739FE9EB22E1
SHA-256485EFA1F754527841C3611BA0831314A494944A122BC24DA48BED8CF127CA8FC