Langue: en

Version: August 1, 2007 (debian - 07/07/09)

Section: 5 (Format de fichier)


shogun - A Large Scale Machine Learning Toolbox


shogun [options]


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.
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 entered


svm-train(1), svm-predict(1). svm-scale(1).


shogun was written by Soeren Sonnenburg <> and Gunnar Raetsch <>

This manual page was written by Soeren Sonnenburg <>, for the Debian project (but may be used by others).