SSVM uses a smooth unconstrained optimization reformulation of the traditional quadratic program. It is solved by a very fast Newton-Armijo algorithm and has been extended to nonlinear separation surfaces by using nonlinear kernel techniques. For more information, see our paper Smooth Support Vector Machines.

SVMs are an optimization based approach for solving machine learning problems. For an introduction to SVMs, you may want to look at this tutorial.

The software is free for academic use. For commercial use, please contact Olvi Mangasarian.

Click here to download the software. The software consists of:

• A linear version of the SSVM
• A nonlinear version of the SSVM

The only software needed to run these programs is MATLAB www.mathworks.com.

If you publish any work based on SSVM, please cite both the software and the paper on which it is based. Here are recommended LaTeX bibliography entries:

@article{lm:99,
author = "Yuh-Jye Lee and O. L. Mangasarian",
title = "{SSVM}: A Smooth Support Vector Machine",
year = 2001,
journal={Computational Optimization and Applications},
volume = {20},
pages = {5-22},
note = {Data Mining Institute, University of Wisconsin,
Technical Report 99-03.
ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/99-03.ps}}