shogun - A Large Scale Machine Learning Toolbox
This manual page briefly documents the readline interface of shogun
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 on-the-fly pre-processing.
A summary of options is included below.
-h, --help, /?
Show summary of options.
-i listen on tcp port 7367 (hex of sg)
execute a script by reading commands from file <filename>
when no options are given the interactive readline interface will be
svm-train(1), svm-predict(1). svm-scale(1).
shogun was written by Soeren Sonnenburg
<Soeren.Sonnenburg@first.fraunhofer.de> and Gunnar Raetsch
This manual page was written by Soeren Sonnenburg <email@example.com>,
for the Debian project (but may be used by others).
August 1, 2007