shogun - A Large Scale Machine Learning Toolbox
This manual page briefly documents the readline interface of shogun
is a large scale 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
A summary of options is included below.
- -h, --help, /?
- Show summary of options.
- listen on tcp port 7367 (hex of sg)
- execute a script by reading commands from file
- when no options are given the interactive readline
interface will be entered
shogun was written by Soeren Sonnenburg
<Soeren.Sonnenburg@first.fraunhofer.de> and Gunnar Raetsch
This manual page was written by Soeren Sonnenburg <firstname.lastname@example.org>, for
the Debian project (but may be used by others).