原文地址：http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html

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Chih-Chung Chang and?Chih-Jen Lin

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?Version 3.21 released on December 14, 2015. It conducts some minor fixes.?
?LIBSVM tools?provides?many extensions?of LIBSVM. Please check it if you need some functions not supported in LIBSVM.?
?We now have a nice page?LIBSVM data sets?providing problems in LIBSVM format.?
?A practical guide to SVM classification?is available now! (mainly written for beginners)?
We now have an easy script (easy.py) for users who know NOTHING about SVM. It makes everything automatic--from data scaling to parameter selection.?
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.

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To see the importance of parameter selection, please see our?guide?for beginners.?
?Using libsvm, our group is the winner of IJCNN 2001 Challenge (two of the three competitions), EUNITE world wide competition on electricity load prediction,?NIPS 2003 feature selection challenge?(third place),?WCCI 2008 Causation and Prediction challenge?(one of the two winners), and?Active Learning Challenge?2010 (2nd place).

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Introduction

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.

Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin.?Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)

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. Main features of?LIBSVM?include

• Different SVM formulations
• Efficient multi-class classification
• Cross validation for model selection
• Probability estimates
• 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,?OCaml,?LabVIEW, and?PHP?interfaces.?C# .NET?code and?CUDA?extension is available.?
  It's also included in some data mining environments:?RapidMiner,?PCP, and?LIONsolver.
• Automatic model selection which can generate contour of cross validation accuracy.

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Download LIBSVM

The current release (Version 3.21, December 2015) of?LIBSVM?can be obtained by downloading the?zip file?or?tar.gz?file. You can also check this?github?directory. 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. For?MS Windows?users, there is a sub-directory in the zip file containing binary executable files. Precompiled Java class archive is also included.

Please read the?COPYRIGHT?notice before using?LIBSVM.?

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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.

Change?Run?Clear?

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-SVC1 -- nu-SVC2 -- one-class SVM3 -- epsilon-SVR4 -- nu-SVR -t kernel_type : set type of kernel function (default 2)0 -- linear: u'*v1 -- polynomial: (gamma*u'*v + coef0)^degree2 -- 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.?

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Additional Information (how to cite LIBSVM)

Frequently Asked Questions (FAQ)?and?Change log

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)

For more information about nu-SVM and one-class SVM?, please see

• B. Sch?lkopf, A. Smola, R. Williamson, and P. L. Bartlett. New support vector algorithms.?Neural Computation, 12, 2000, 1207-1245.
• B. Sch?lkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution.?Neural Computation, 13, 2001, 1443-1471.

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Interfaces and extensions to LIBSVM

LanguageDescriptionMaintainers and Their AffiliationSupported LIBSVM versionLink

Java	Java code close to LIBSVM C code.	LIBSVM authors at National Taiwan University.	The latest	Included inLIBSVM?package
Java	Refactored Java code for faster training/testing.	David Soergel?at University of California, Berkeley.	2.88	jlibsvm
MATLAB and OCTAVE	A simple MATLAB and OCTAVE interface	LIBSVM authors at National Taiwan University.	The latest	Included inLIBSVM?package
MATLAB	An old version (no longer available)	Junshui Ma and Stanley Ahalt at Ohio State University	2.33	Dead Link
R	Please install by typing?install.packages('e1071')?at R command line prompt. (document?and?examples).	David Meyer?at the Wirtschaftsuniversit?t Wien (Vienna University of Economics and Business Administration)	3.17	WWW
Python	A python interface has been included in LIBSVM since version 2.33.	Initiated by?Carl Staelin?at HP Labs. Updated/maintained by LIBSVM authors.	The latest	Included inLIBSVM?package
Python and C#	Interfaces provided in the framework pcSVM	Uwe Schmitt from Germany	2.71	pcSVM
Perl	?	Matthew Laird at Simon Fraser University, Canada and Saul Rosa	3.12	perl-libsvm
Ruby	Ruby language bindings for LIBSVM	C. Florian Ebeling and Rimas Silkaitis	3.18	rb-libsvm
Ruby	A Ruby interface via SWIG	Tom Zeng	2.9	libsvm-ruby-swig
Weka	?	Yasser EL-Manzalawy and Vasant Honavar at Iowa State University.	2.8	WLSVM
Node.js	?	Nicolas Panel	3.18	Node.js interface
Scilab	?	Holger Nahrstaedt from the Technical University of Berlin	3.11	Scilab interface
Common LISP	Common Lisp wrapper of LIBSVM	Gábor Melis	2.88	Common LISP wrapper
CLISP	An?FFI-based interface distributed with CLISP	Sam Steingold	2.9	CLISP LibSVM module
Haskell	A Haskell binding to LIBSVM	Paulo Tanimoto	3.1	Haskell binding
OCaml	A OCaml binding to LIBSVM	Oliver Gu	3.16	OCaml binding
Nimrod	LIBSVM Wrapper for?Nimrod	Andreas Rumpf	3.12	libsvm wrapper
.NET	LIBSVM for .NET	Nicolas Panel	3.17	libsvm-net
.NET	.NET conversion of LIBSVM	Matthew Johnson	2.89	SVM.NET
CUDA	LIBSVM Accelerated with GPU using the CUDA Framework	A. Athanasopoulos, A. Dimou, V. Mezaris, and I. Kompatsiaris at CERTH-ITI	3.0	MKLAB
Cell	LIBSVM Accelerated using Cell Processors	Moreno Marzolla?at University of Bologna, Italy	2.89	libsvm_CBE
Labview	LabView interface to LIBSVM. Both Windows/Linux are supported.	Oystein Sture	3.20	LabView interface
C#	C# wrapper of libsvm	Can Erhan	3.20	github directory
PHP	LIBSVM binding for PHP	Ian Barber	The latest (LIBSVM must be installed first)	PHP binding
Android	LIBSVM on Android	Yu-Chih Tung at Univ of Michigan	3.20	LIBSVM on Androis

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Acknowledgments: This work was supported in part by the National Science Council of Taiwan via the grant NSC 89-2213-E-002-106. The authors thank?their group members and users?for helpful discussion and comments. Please send comments and suggestions to?Chih-Jen Lin.

轉(zhuǎn)載于:https://www.cnblogs.com/davidwang456/articles/5586277.html

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