The Shogun Machine Learning Toolbox

Summary

SHOGUN is a large Scale Machine Learning Toolbox, being implemented in C++ and offering interfaces to C#, Java, Lua, Octave, Perl, Python, R and Ruby.

Owner

Name: Björn Esser
Email: besser82@fedoraproject.org
Release notes owner:

Current status

Targeted release: Fedora 21
Last updated: 2013-12-15
Tracker bug: #1098143

Detailed Description

Homepage: The SHOGUN Machine Learning Toolbox
SCM-repo: on GitHub
Documentation: is available here
further Information: on Wikipedia

The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.

Benefit to Fedora

This will bring a Machine Learning Toolkit with a unique selection and versatility to Fedora.