Mathematica Journal
Volume 10, Issue 1


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A Flexible Implementation for Support Vector Machines
Roland Nilsson
Johan Björkegren
Jesper Tegnér


In this article, we have demonstrated the utility of the MathSVM package for solving pattern recognition and regression problems. This is an area of very active research and these algorithms are evolving quickly. In a rapidly moving field such as this, it is important to have a clear, well documented, high-level approach to implementation to minimize confusion. Mathematica provides an excellent solution here, due to its high-level programming language and symbolic capabilities.

MathSVM is currently 100% native Mathematica code, written with the emphasis on clarity. This does incur penalties in terms of computational speed. Some parts of the QP algorithm are therefore being ported to Java at this time to improve performance. This should not impair the clarity of the software in any way, since the QPSolve function is easy separable from the other parts of MathSVM in a "black box" fashion.

The MathSVM software is still in its infancy and will no doubt expand rapidly, as our group is currently involved in many projects in pattern recognition and high-dimensional data analysis in general, as well as in a biomedical context. We hope that this contribution will initiate other efforts to bring understandable implementations of machine learning algorithms to the Mathematica community.

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