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libsvm and liblinear

(4095 downloads for this version - 25465 downloads for all versions)
Details
Version
1.4.5
Authors
Holger Nahrstaedt
Chih-Chung Chang
Chih-Jen Lin
Owner Organization
Technische Universitaet Berlin
Maintainer
Holger Nahrstaedt
License
Creation Date
March 25, 2015
Source created on
Scilab 5.4.x
Binaries available on
Scilab 5.4.x:
Linux 32-bit Windows 32-bit Windows 64-bit MacOSX Linux 64-bit
Scilab 5.5.x:
Windows 64-bit MacOSX Linux 32-bit Windows 32-bit Linux 64-bit
Install command
--> atomsInstall("libsvm")
Description
            This tool provides a simple interface to LIBSVM, a library for support vector
machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm).
It is very easy to use as
the usage and the way of specifying parameters are the same as that of LIBSVM.

This tool provides  also a simple interface to LIBLINEAR, a library for
large-scale regularized linear classification (http://www.csie.ntu.edu.tw/~cjlin/liblinear).
 It is very easy to use as the usage and the way of specifying parameters are
the same as that of LIBLINEAR.

This Toolbox is compatible with the NaN-toolbox!

Changelog
============
1.4.5
 - libsvm_loadmodel and libsvm_savemodel fixed
 - st_deviation renamed to stdev
1.4.4
 - 2nu-SVM added http://www.ece.rice.edu/~md/np_svm.php
 - LIBLINEAR is updated to 1.94
 - LIBSVM is updated to 3.20
 - some bugfixes
 - The crossvalidation (libsvm_svmtrain with "-v 5") result is now a
vector with [Cross Validation Accuracy, Positive Cross Validation Accuracy,
Negative Cross Validation Accuracy]
1.4.3
 - Depreated stack-c function were removed
1.4.2
 - new functions libsvm_savemodel and libsvm_loadmodel
1.4.1
 - buxfix for getpath
 - help files fixed for libsvm_linpredict and libsvm_lintrain
 - path operations are replaced by fullfile

1.4.0
 - unit tests for libsvmwrite and libsvmread
 - fix issues 805, 806, 808, 809, 813, 814, 
 - renaming of the following functions:
   *svmtrain > libsvm_svmtrain
   *svmpredict > libsvm_svmpredict
   *train > libsvm_lintrain
   *predict > libsvm_linpredict
   *svmconfmat > libsvm_confmat
   *svmgrid > libsvm_grid
   *svmgridlinear > libsvm_gridlinear
   *svmnormalize > libsvm_normalize
   *svmpartest > libsvm_partest
   *svmrocplot > libsvm_rocplot
   *svmscale > libsvm_scale
   *svmtoy > libsvm_toy
1.3.1
 - compatible with scilab-5.4.0-beta-1 and  scilab-5.4.0-alpha-1 or lower
1.3
 - compatible with scilab-5.4.0-beta-1
 - incompatible with scilab-5.4.0-alpha-1 and lower
 - fix several bugs in examples
 - fix precomputed kernel bug in svmtrain
 - LIBLINEAR is updated to 1.91
 - LIBSVM is updated to 3.12
1.2.2
 - some bug fixes
 - help files improved
1.2.1
 - svmtoy added
 - improved error handling in sci_gateway
 - improved help files
 - bug in performance demo removed
1.2
 - the Nan-Toolbox 1.3 is compatible to this toolbox now!
 - improved help-files
 - improved demos
 - LIBLINEAR with optional instance weight support
1.1
 - improved demos
 - works under Windows
 - new function: svmnormalize 
1.0
 - first release of libsvm - toolbox




This interface was initially written by Jun-Cheng Chen, Kuan-Jen Peng,
Chih-Yuan Yang and Chih-Huai Cheng from Department of Computer
Science, National Taiwan University. 

It was converted to Scilab 5.3 by Holger Nahrstaedt from TU Berlin.

If you find this tool useful, please cite LIBSVM as follows

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support
vector machines. ACM Transactions on Intelligent Systems and
Technology, 2:27:1--27:27, 2011. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm

Please cite LIBLINEAR as follows

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin.
LIBLINEAR: A Library for Large Linear Classification, Journal of
Machine Learning Research 9(2008), 1871-1874.Software available at
http://www.csie.ntu.edu.tw/~cjlin/liblinear

            
Files (11)
[178.25 kB]
Source code archive

[222.27 kB]
Linux 32-bit binary for Scilab 5.4.x
Linux 32-bit
Automatically generated by the ATOMS compilation chain

[251.31 kB]
Windows 32-bit binary for Scilab 5.4.x
Windows 32-bit
Automatically generated by the ATOMS compilation chain

[264.01 kB]
Windows 64-bit binary for Scilab 5.4.x
Windows 64-bit
Automatically generated by the ATOMS compilation chain

[218.32 kB]
MacOSX binary for Scilab 5.4.x
MacOSX version
Automatically generated by the ATOMS compilation chain

[230.05 kB]
Linux 64-bit binary for Scilab 5.4.x
Linux 64-bit
Automatically generated by the ATOMS compilation chain

[232.19 kB]
Windows 64-bit binary for Scilab 5.5.x
Windows version (x64)
Automatically generated by the ATOMS compilation chain

[200.74 kB]
MacOSX binary for Scilab 5.5.x
MacOSX version
Automatically generated by the ATOMS compilation chain

[203.00 kB]
Linux 32-bit binary for Scilab 5.5.x
Linux version (i686)
Automatically generated by the ATOMS compilation chain

[221.50 kB]
Windows 32-bit binary for Scilab 5.5.x
Windows version (i686)
Automatically generated by the ATOMS compilation chain

[210.62 kB]
Linux 64-bit binary for Scilab 5.5.x
Linux version (x86_64)
Automatically generated by the ATOMS compilation chain

News (0)
Comments (1)     Leave a comment 
Comment from Marc Albertelli -- April 21, 2015, 10:54:13 AM    
Hi,
Unlike to classication, the "probability estimates" options doesn't work with
SVR
(regression).
The svm_predict function returns a null array for the "decision_values"
variable.
Thanks a lot for your help,
Regards,
M.

ps : see below a very simple example (derived from the demos) that illustrates the problem
: 

N = 20;
M = 1;
t = rand(N,1,'norm');

m = 1//:10:100;
x = [t];
for ii=1:M-1
    x = [x  t+ii*rand(N,1,'norm')/2];
end

min(x,'r'))),size(x,1),1);
x=libsvm_scale(x,[0 1]);
y = 2*t + rand(N,1,"norm")/2 + 7;

model = libsvm_svmtrain(y(:),x(:,:),'-s 4 -t 2 -n 0.5 -c 1 -b 1');
[predicted_label, accuracy, decision_values] = libsvm_svmpredict(y(:),x(:,:), model, '-b
1');

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