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 csharp package employing swig.