The new release
of LIBSVM largely improves
the implementation of nu-SVM.
By a serious timing comparison with C-SVM, we have
shown the practical viability of nu-SVM.
For details, please see the revised version
of the paper
nu-Support Vector Classifiers:
Theory and Algorithms.
The new release
is an integrated tool for
vector classification, regression
as well as two varients:
All these different problems are
in one short file
(1000-line C++ code).
here to see
more new features of version 2.0.
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 and regression: LIBSVM.
Its basic algorithm is a simplification of both SMO by Platt
It is also
a simplification of the
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.
a graphic interface to
demonstrate 2-D pattern recognition.
The current release (Version 2.0) of LIBSVM can be obtained
by downloading the
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
a file scale.c for scaling training data. A README file with detailed explanation
is also provided.
MS Windows users, there
is a subdirectory in the zip file
containing binary executable files.
Please read the COPYRIGHT notice before using
We provide a simple graphic interface
for 2-D pattern
Examples of using this tool
are as follows:
To install this tool, please read
the README file in the
References of LIBSVM:
For more information about nu-SVM and one-class SVM , please see
- Schölkopf, A. Smola, R. Williamson, and P. L. Bartlett.
New support vector algorithms.
Neural Computation, 12:1207:1245.
Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and
R. C. Williamson.
Estimating the support of a
Technical Report 99-87, Microsoft Research, 1999.
A MATLAB interface of LIBSVM (currently libsvm 2.0 is used) 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
If you have any problems using LIBSVM, we are happy to provide help. Please send comments and suggestions to Chih-Jen
This work was supported in part by
the National Science Council of Taiwan via the grant
The authors thank Chih-Wei Hsu and
for many helpful discussions and comments.