Tool: SHOGUN aims high with Google Summer of Code ))

Dim reduction is an important technique for high d viz. 

SHOGUN aims high with Google Summer of Code

Google Summer of Code 2011 gave a big boost to the development of the SHOGUN machine learning toolbox. In case you have never heard of SHOGUN or machine learning, machine learning involves algorithms that do ‘intelligent’ and even automatic data processing and is currently used in many different settings. You will find machine learning in the face detection in your camera, compressing the speech in your mobile phone, and powering the recommendations in your favorite online shop, as well as predicting the solubility of molecules in water and the location of genes in humans, to name just a few examples. Interested? Shogun can help you give it a try.

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.

Some very simple examples stemming from a sub-branch of machine learning called supervised learning illustrate how objects represented by two-dimensional vectors can be classified into good or bad, by learning a support vector machine. I would suggest installing the python_modular interface of SHOGUN and to run the example interactive_svm_demo.py also included in the source tarball. Two images illustrating the training of a support vector machine follow:

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