To read my blog about SHOGUN development, click here.
SHOGUN (website) is a machine learning toolbox with focus is on large scale kernel methods and especially on Support Vector Machines. It provides a generic SVM interface for several different SVM state-of-the-art implementations
Each of the SVMs can be combined with a variety of kernels. The toolbox provides efficient implementations of many common kernels.
Also many other popular machine learning algorithms are implemented and the list is continuously extended for example due to the support of the Google Summer of Code. For example, there are now Gaussian processes, many dimensionality reduction methods, Structured Output and latent SVMs, various multi-task learning techniques, and many more.
SHOGUN is implemented in C++ and comes with interfaces to many languages.
I got into the team after the GSoC 2011 and since then have implemented some new features: A framework for cross-validation and model selection during the GSoC 2011 and a framework for kernel based statistical hypothesis testing in the GSoC 2012. I also worked on migrating serialized SHOGUN objects from different versions to one another.