LIBSVM
Most available support vector machines (SVM) software are either quite
complicated or are not suitable for large problems. Instead of seeking
a very fast software for difficult problems, we provide a simple, easy-to-use,
and moderately efficient software for SVM classification: LIBSVM.
It is a simplification of both SMO by Platt
and SVMLight
by
Joachims.
LIBSVM is also
a simplification of the
modification 2
of SMO by
Keerthi et al.
Our goal is to help users from other fields to
easily use SVM as a tool.
LIBSVM provides a simple interface
where users can easily link it with their own programs.
In addition,
we provide
a graphic interface to
demonstrate 2-D pattern recognition.
Download LIBSVM
The current release (Version 1.04) of LIBSVM can be obtained
by downloading the zip
file. (Due to possible slow connection, you may want to download it from other places:
US Download. Please e-mail us if you have problems
to download the file.)
The package includes the major library (svm.c and svm.h), two examples
(svm-train.c and svm-classify.c) demonstrating the use of
LIBSVM, and
a file scale.c for scaling training data. A README file with detailed explanation
is also provided.
For
MS Windows users, there
is a subdirectory in the zip file
containing binary executable files.
Please read the COPYRIGHT notice before using
LIBSVM.
Graphic Interface
We provide a simple graphic interface
for 2-D pattern
recognition.
Examples of using this tool
are as follows:
To install this tool, please read
the README file in the
package.
Additional Information
For additional information (algorithms and benchmarks) on LIBSVM,
please see the paper
LIBSVM:
Introduction and Benchmarks.
A MATLAB interface of LIBSVM has been done by
Junshui Ma and Stanley Ahalt
at Ohio State University.
One of our previous SVM software which focuses on difficult SVM models
is BSVM.
If you have any problems using LIBSVM, we are happy to provide help. Please send comments and suggestions to Chih-Jen
Lin.
Please also e-mail us if you would like
be informed of future LIBSVM software updates.
Acknowledgments:
This work was supported in part by
the National Science Council of Taiwan via the grant
NSC 89-2213-E-002-013.
The authors thank Chih-Wei Hsu and
Jen-Hao Lee
for many helpful discussions and comments.