Download Source Package shogun:
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 modular Octave package employing swig.
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| Architecture | Package Size | Installed Size | Files |
|---|---|---|---|
| alpha | 4,718.9 kB | 18248 kB | [list of files] |
| amd64 | 4,240.3 kB | 14392 kB | [list of files] |
| arm | 4,604.3 kB | 15244 kB | [list of files] |
| armel | 3,887.3 kB | 12872 kB | [list of files] |
| hppa | 4,553.6 kB | 21688 kB | [list of files] |
| i386 | 4,074.3 kB | 13296 kB | [list of files] |
| ia64 | 5,357.0 kB | 26528 kB | [list of files] |
| mips | 3,282.4 kB | 17156 kB | [list of files] |
| mipsel | 3,223.6 kB | 17156 kB | [list of files] |
| powerpc | 4,082.9 kB | 14216 kB | [list of files] |
| s390 | 3,870.1 kB | 14576 kB | [list of files] |
| sparc | 3,909.5 kB | 13780 kB | [list of files] |