Part of my brain research and machine learning endeavors is to use SVM for the purpose of training and/or testing in real time. given the huge amount of temporal brain data that we acquire in real-time,  there is a need for a large scale SVM algorithm. usually SVM can handle thousands of samples with thousands of features, and with a cutting edge PC  you may be able to scale it up to ~10  times as much. but using shogun you can process more data, the shogun team reports up to 10 millions training samples and up to 7 billion test samples.

A few months back shogun did not support C# . Daniel Korn and I made some advances to create something called an interface file for shogun, that will make it work with C#.  half way through the process one of the shogun participants in google summer of code took control of the interface programming and finished it. with this new release there is a C# dll that everyone can use, and in it there is support for many data types.  i still need to figure out if it will support sparse lists/dictionaries so that the memory footprint will be small enough for any algorithm. with the new dll there are many C# examples files that we have created to test out the new interface/dll.

 

shogun 1.1.0 can be downloaded here.

our credits appear in the NEWS section of the release and on the front page.

Features:

• New dimensionality reduction algorithms: Diffusion Maps, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Neighborhood Preserving embedding, Locality Preserving Projections.
• Various performance improvements for dimensionality reduction methods (BLAS, alignment formulation of the LLE, ..)
• Automatical k determination mode for Locally Linear Embedding dimension reduction method based on reconstruction error.
• ARPACK and SUPERLU integration.
• Introduce the concept of Converters that can embed (arbitrary) feature types into different feature types.
• LibSVM is now pthread-parallelized.
• Create modshogun.dll for csharp.
• Various new c# examples (thanks Daniel Korn and Ori Cohen).
• Dimensionality reduction examples application is introduced