The NSVM uses an implicit Lagrangian formulation of a support vector machine classifier that led to a highly effective iterative scheme. It is then solved by a finite Newton method. For more information, see our paper Finite Newton Method for Lagrangian 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 NSVM
• A nonlinear version of the NSVM

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

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

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