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
FileSize	29066006
MD5	B7CABCB5226EBCC6BB6586B5A2AB0270
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 Chinese user and developer documentation.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-doc-cn
PackageSection	doc
PackageVersion	3.2.0-7.3
SHA-1	C25C54005E66AF3DF57700F46F2FCFCF470EC6A2
SHA-256	24D7C739A875CE1EDFA162D3B0527894A293AE384902AA2FE4D52AB97C335053

Key	Value
FileSize	29119232
MD5	F8711E3571D12DFA5B991E056314E994
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 English user and developer documentation.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	shogun-doc-en
PackageSection	doc
PackageVersion	3.2.0-7.3
SHA-1	7A5D1EFDB2818B39BC6BAE1C30B128999791CB7C
SHA-256	EFED65FCE7B2A82C724AFC078A65AB18CC7445BA035B9313DF20BF31F05A0743