Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	75633244
MD5	861C2B0635123A4CA5B373856E90562F
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	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun-dbg
PackageSection	debug
PackageVersion	3.2.0-8+b1
SHA-1	2E187DA3081BC0B148A2C690D053252B8DB04E3F
SHA-256	E219EDE62BA3B7DCF85750D6CD57A49E7C71DFA27B680EDD1971975110A8716F

Key	Value
FileSize	1531728
MD5	36D1478C674861CFAB02D7D2234BF951
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	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-8+b1
SHA-1	F6A4879FE6295AA7153AA64767A7F1FAFDBF1809
SHA-256	DA2E32C0A7F00FC18B66CA9932852A8F2F64AC52C96261894A12174BB5CCB8E0

Key	Value
FileSize	959012
MD5	416CFAC0C48CFC688FDC7963E6A2103A
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	Debian QA Group <packages@qa.debian.org>
PackageName	shogun-cmdline-static
PackageSection	science
PackageVersion	3.2.0-8+b1
SHA-1	C6C5405AFCF6FFFF39B4E3AB3083A08F5BB4F00B
SHA-256	FF87F5E9C7D7181A0CC7EB3DE78148F5B93D67B2256C9B89EAD2810758509AD9

Key	Value
FileSize	3534684
MD5	F67F03D8B76443CEEC3DD730B454B0EF
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	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun16
PackageSection	libs
PackageVersion	3.2.0-8+b1
SHA-1	FB016F2622472AE0B26C161348FBE053E1F1B840
SHA-256	22B641ADA0155254AA6B99640272C80729A029BC9834EFB7CF12B8F7C480DD75