Package: libshogun-dbg (3.2.0-8 and others)

[ Source: shogun  ]

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.

Tags: Role: Debugging symbols

Other Packages Related to libshogun-dbg

• dep: libshogun16 (= 3.2.0-8+b1)

  Large Scale Machine Learning Toolbox

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Architecture	Version	Package Size	Installed Size	Files
amd64	3.2.0-8+b1	73,087.8 kB	77,230.0 kB	[list of files]
arm64	3.2.0-8+b1	73,860.6 kB	78,569.0 kB	[list of files]
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