Provided by: shogun-cmdline-static_3.2.0-9ubuntu1_amd64
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) filename execute a script by reading commands from file <filename> when no options are given the interactive readline interface will be entered SEE ALSO svm-train(1), svm-predict(1). svm-scale(1). AUTHOR shogun was written by Soeren Sonnenburg <Soeren.Sonnenburg@first.fraunhofer.de> and Gunnar Raetsch <Gunnar.Raetsch@tuebingen.mpg.de> This manual page was written by Soeren Sonnenburg <email@example.com>, for the Debian project (but may be used by others). August 1, 2007 SHOGUN(5)