| Key | Value |
|---|---|
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| MD5 | B4E5C685A7FE9148CBB4A2CA0903BBBB |
| 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 | Soeren Sonnenburg <sonne@debian.org> |
| PackageName | libshogun-dbg |
| PackageSection | debug |
| PackageVersion | 3.2.0-7.3 |
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| SHA-256 | 1CBBBCEB1B81298DBF6AE7310A65F25B137E49D5E90F3A27A21ED7881BE5BC5A |
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| hashlookup:trust | 50 |
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| Key | Value |
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| Key | Value |
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