LPSVM uses a fast Newton method that suppresses input space features. This stand-alone method can handle classification problems in very high dimensional spaces and generates a classifier that depends on very few input features. For more information, see our paper A Feature Selection Newton Method for Support Vector Machine Classification.

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 LPSVM
• A nonlinear version of the LPSVM

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

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

@techreport{fm:02b,
author = "G. Fung and O. L. Mangasarian ",
title = "A Feature Selection Newton Method for Support Vector Machine Classification",
institution = "Data Mining Institute, Computer Sciences Department, University of Wisconsin",
month = {September},
year = 2002,
number = {02-03},
address = "Madison, Wisconsin",
note={ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/02-01.ps}}