萝卜酒博客,师父:LIBSVM 1-- A Library for Support Vector Machin...

LIBSVM -- A Library for Support Vector Machines

Chih-Chung Chang and Chih-Jen Lin

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
• Weighted SVM for unbalanced data
• Both C++ and Java sources
• GUI demonstrating SVM classification and regression
• Python, R (also Splus), MATLAB, Perl, Ruby, Weka, CLISP and LabVIEW interfaces. C# .NET code is available.
  It‘s also included in some learning environments: YALE and PCP.
• Automatic model selection which can generate contour of cross valiation accuracy.

Download LIBSVM

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 subdirectory in the zip file containing binary executable files. Precompiled Java class archive is also included.

Please read the COPYRIGHT notice before using LIBSVM. 

Graphic Interface

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/k)-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.option -v randomly splits the data into n parts and calculates crossvalidation accuracy/mean squared error on them.

To install this tool, please read the README file in the package. There are Windows, X, and Java versions in the package.

Additional Information (how to cite LIBSVM)

References of LIBSVM:

• A guide for beginners:
  C.-W. Hsu, C.-C. Chang, C.-J. Lin. A practical guide to support vector classification
• For implementation details of LIBSVM, please see the document
  LIBSVM : a library for support vector machines.
  pdf, ps.gz.
  or
  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)

• B. Schölkopf, A. Smola, R. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12, 2000, 1207-1245.
• B. Schölkopf, 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.

Interfaces to LIBSVM