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 Python package employing swig.
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| Architecture | Package Size | Installed Size | Files |
|---|---|---|---|
| alpha | 5,212.7 kB | 21244 kB | [list of files] |
| amd64 | 5,114.3 kB | 18868 kB | [list of files] |
| arm | 5,069.6 kB | 19120 kB | [list of files] |
| armel | 4,821.6 kB | 17224 kB | [list of files] |
| hppa | 5,595.3 kB | 24408 kB | [list of files] |
| i386 | 4,965.5 kB | 18168 kB | [list of files] |
| ia64 | 6,450.8 kB | 33088 kB | [list of files] |
| mips | 4,243.0 kB | 22568 kB | [list of files] |
| mipsel | 4,169.4 kB | 22576 kB | [list of files] |
| powerpc | 5,290.6 kB | 19808 kB | [list of files] |
| s390 | 4,531.1 kB | 19016 kB | [list of files] |
| sparc | 5,028.4 kB | 19020 kB | [list of files] |