The 
Mathematica Journal
Volume 10, Issue 1

Search

In This Issue
Articles
Trott's Corner
New Products
New Publications
Calendar
News Bulletins
New Resources
Classifieds

Download This Issue 

About the Journal
Editorial Policy
Staff and Contributors
Submissions
Subscriptions
Advertising
Back Issues
Contact Information

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

Conclusion

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.



     
About Mathematica | Download Mathematica Player
© Wolfram Media, Inc. All rights reserved.