LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Version 3.11 released on November 5, 2011
The parameter selection tool grid.py generates the following contour of cross-validation accuracy. To use this tool, you also need to install python and gnuplot
LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include
- Different SVM formulations
- Efficient multi-class classification
- Cross validation for model selection
- Various kernels (including precomputed kernel matrix)
- Weighted SVM for unbalanced data
- Both C++ and Java sources
- GUI demonstrating SVM classification and regression
- Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available.
- It's also included in some data mining environments: RapidMiner and PCP.
- Automatic model selection which can generate contour of cross valiation accuracy.
Download LIBSVM.
The current release (Version 3.11, November 2011) of LIBSVM can be obtained by downloading the zip file or
tar.gz file. Please e-mail us if you have problems to download the file.
The package includes the source code of the library in C++ and Java, and a simple program for scaling training data. A README file with detailed explanation is provided.
Please read the
COPYRIGHT notice before using
LIBSVM.
Graphic Interface.
Here is a simple applet demonstrating SVM classification and regression.
Click on the drawing area and use ``Change'' to change class of data. Then use ``Run'' to see the results.
Examples of options: -s 0 -c 10 -t 1 -g 1 -r 1 -d 3
Classify a binary data with polynomial kernel (u'v+1)^3 and C = 10
options:
-s svm_type : set type of SVM (default 0)
0 -- C-SVC
1 -- nu-SVC
2 -- one-class SVM
3 -- epsilon-SVR
4 -- nu-SVR
-t kernel_type : set type of kernel function (default 2)
0 -- linear: u'*v
1 -- polynomial: (gamma*u'*v + coef0)^degree
2 -- radial basis function: exp(-gamma*|u-v|^2)
3 -- sigmoid: tanh(gamma*u'*v + coef0)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/num_features)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 100)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)
-wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1)
The k in the -g option means the number of attributes in the input data.
To install this tool, please read the README file in the package. There are Windows, X, and Java versions in the package.
Frequently Asked Questions (FAQ)
References of
LIBSVM:
- Official implementation document:
C.-C. Chang and C.-J. Lin. LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
pdf, ps.gz, ACM digital lib.
- Instructions for using LIBSVM are in the README files in the main directory and some sub-directories.
- A guide for beginners:
C.-W. Hsu, C.-C. Chang, C.-J. Lin. A practical guide to support vector classification
- An introductory video for windows users.
- Other implementation documents:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there.
- Other documents written by users. (including some non-English documents)
If you liked this article, subscribe to the feed by clicking the image below to keep informed about new contents of the blog:
Posts
