Iinstead of a standard support vector machine that classifies points by assigning them to one of two disjoint half-spaces, PSVM classifies points by assigning them to the closest of two parallel planes. For more information, see our paper Proximal 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 PSVM
• A nonlinear version of the PSVM

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

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

@inproceedings{fm:01,
author = "G. Fung and O. L. Mangasarian",
title = "Proximal Support Vector Machine Classifiers",
editor = "F. Provost and R. Srikant",
booktitle = {Proceedings KDD-2001: Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA},
year = 2001,
pages = {77-86},
address = {New York},
publisher = {Asscociation for Computing Machinery},
note = {ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-02.ps}}