Result for A84C22D6DAC1DCE8C4437165B634F7F8306E33DB

Query result

Key Value
FileName./usr/share/doc/libshogun15/changelog.Debian.gz
FileSize1366
MD5320DAA5F3EB9C2A736663B401F794C82
SHA-1A84C22D6DAC1DCE8C4437165B634F7F8306E33DB
SHA-256EF1C0A20E63A22C72DF210D635421F5DA239EFD2236838065A82735FBDB908A6
SSDEEP24:Xww4Lt3VzQAJhQ0Vm/W4B3IlG2IO2hYVejLJWZRfKvpkaR/s:XX8t3nJEe23uIOaYIpkaRU
TLSHT12321D8947B8596038E5E911EEE4AA21B226C0DB98E27D38239928570272B93C4CD1D10
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

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
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