Once more GSoC finished leaving Shogun with a bunch of new functionalities. No less than eight projects have been realized this summer by as many talented students. Do not hesitate and dive in the GSoC 2012 follow-up article to sate your curiosity concerning all the new features that have been included in Shogun!
In case you are a talented student interested in a summer project, we are looking for you! Summer of Code 2012 we aim at
|Soeren Sonnenburg, Gunnar Raetsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, and Vojtech Franc. The SHOGUN Machine Learning Toolbox . Journal of Machine Learning Research, 11:1799-1802, June 2010.|
A few weeks have passed since SHOGUN has been accepted for Google Summer of Code 2012. Student application deadline was Easter Friday (April 6) and shogun received 48 proposals from 38 students. Some more detailed stats can be found in the figure below.
This is a drastic drop compared with last year (about 60 students submitted 70 proposals). However, this drop can easily be explained: To even apply we required a small patch, which is a big hurdle.
Nevertheless, about a dozen of proposals didn't come with a patch (even though written on the instructions page that this is required) - an easy reject. In the end the quality of proposals increased a lot and we have many very strong candidates this year. Now we will have to wait to see how many slots we will receive before we can finally start the fun :-)
SHOGUN is a machine learning toolbox, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines for classification and regression, hidden Markov models, multiple kernel learning, linear discriminant analysis, linear programming machines, and perceptrons. Most of the specific algorithms are able to deal with several different data classes, including dense and sparse vectors and sequences using floating point or discrete data types. We have used this toolbox in several applications from computational biology, some of them coming with no less than 10 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond.
SHOGUN is implemented in C++ and interfaces to all important languages like MATLAB, R, Octave, Python, Lua, Java, C#, Ruby and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org.
During Summer of Code 2012 we are looking to extend the library in three different ways: