libshogun11¶

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 core library with the machine learning methods and ui helpers all interfaces are based on.

Related packages: libshogun-dev, shogun-cmdline-static, shogun-csharp-modular, shogun-dbg, shogun-doc-cn, shogun-doc-en, shogun-elwms-static, shogun-java-modular, shogun-lua-modular, shogun-python-modular, shogun-python-static, shogun-r-static, shogun-ruby-modular