ASVR is a fast technique for linear support vector regression, based on an active set approach which results in very fast running times. For more information, see our paper Active Set Support Vector Regression.
Support Vector Regression is an optimization based approach for solving machine learning regression problems, based on Support Vector Machines. For an introduction to SVR and SVMs, you may want to look at this tutorial.
The software is free for academic use. For commercial use, please contact Dave Musicant.
Click here to download the software. The software consists of:
No additional software whatsoever is required to use these tools.
If you publish any work based on ASVR, please cite both the software and the paper on which it is based. Here are recommended LaTeX bibliography entries:
@misc{asvr,
author = "David R. Musicant and Alexander Feinberg",
title = {{ASVR Software:} Active Set Support Vector Machine Regression Software},
year = 2002,
institution = {Department of Mathematics and Computer Science, Carleton
College},
note = { http://www.mathcs.carleton.edu/faculty/dmusican/asvr}}
@techreport{asvrpaper,
author = "David R. Musicant and Alexander Feinberg",
title = "Active Support Vector Machine Classification",
institution = "Department of Mathematics and Computer Science, Carleton
College",
month = {July},
year = 2002,
number = {01-02},
address = "Northfield, Minnesota",
note={http://www.mathcs.carleton.edu/faculty/dmusican/tr0102.ps}}
For more information, contact:
David R. Musicant
dmusican@carleton.edu