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ATOMS : libsvm and liblinear details

libsvm and liblinear

Libraries for SVM and large-scale linear classification
(14078 downloads for this version - 77838 downloads for all versions)
Details
Version
1.4.5
A more recent valid version exists: 1.5
Authors
Holger Nahrstaedt
Chih-Chung Chang
Chih-Jen Lin
Owner Organization
Technische Universitaet Berlin
Maintainers
Holger Nahrstaedt
Administrator ATOMS
Chin Luh Tan
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 macOS Linux 64-bit
Scilab 5.5.x:
Windows 64-bit macOS 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
Upload date : 2015年03月26日 11:04:58
MD5 : 7b3a32d4322f134d786f046c7d5cbb35
SHA1 : b4995b1076b8bb565cb07f89158742684444af60
Downloads : 854
 File list
 
[222.27 kB]
Linux 32-bit binary for Scilab 5.4.x
Linux 32-bit
Automatically generated by the ATOMS compilation chain
Upload date : 2015年04月10日 08:26:28
MD5 : fe72e22ce788a7d38298bd10e2449cda
SHA1 : 9eba11b17af5ea180a1163419c4ad360e1d0f2e0
Downloads : 911
 File list
 
[251.31 kB]
Windows 32-bit binary for Scilab 5.4.x
Windows 32-bit
Automatically generated by the ATOMS compilation chain
Upload date : 2015年04月10日 08:32:25
MD5 : 3aa0e950020e45522eebfc306715ec14
SHA1 : 13e92f1a811aff8402a9635e54136808d300b526
Downloads : 740
 File list
 
[264.01 kB]
Windows 64-bit binary for Scilab 5.4.x
Windows 64-bit
Automatically generated by the ATOMS compilation chain
Upload date : 2015年04月10日 08:32:30
MD5 : d92b75d632d740a09fc394bf41f651c3
SHA1 : 5095b02b3165745571e34a8477ee15d140d2745f
Downloads : 1281
 File list
 
[218.32 kB]
macOS binary for Scilab 5.4.x
MacOSX version
Automatically generated by the ATOMS compilation chain
Upload date : 2015年04月10日 08:28:55
MD5 : db6b08bc877d7d3571be635032d0663c
SHA1 : 6512b2dc3ce8f7633ba43b3efdf4f2b94b09059a
Downloads : 962
 File list
 
[230.05 kB]
Linux 64-bit binary for Scilab 5.4.x
Linux 64-bit
Automatically generated by the ATOMS compilation chain
Upload date : 2015年04月10日 08:29:45
MD5 : 5eb5731b0301692f24b9b5969035355f
SHA1 : 23f71631b3d557c38ea2984008cb1bf873e10c7f
Downloads : 1349
 File list
 
[232.19 kB]
Windows 64-bit binary for Scilab 5.5.x
Windows version (x64)
Automatically generated by the ATOMS compilation chain
Upload date : 2016年06月22日 17:38:32
MD5 : dffd2621123ee531fc0b647b564e00bf
SHA1 : 4dea8afd2fb9387b222efbd194d0f56f6040e40e
Downloads : 2735
 
[200.74 kB]
macOS binary for Scilab 5.5.x
MacOSX version
Automatically generated by the ATOMS compilation chain
Upload date : 2016年06月22日 17:38:33
MD5 : 71f3349294795bea01f0eb8257d10310
SHA1 : 83fc0b5a6b18054e3f931eca96f8a8eaffc7dcb4
Downloads : 786
 
[203.00 kB]
Linux 32-bit binary for Scilab 5.5.x
Linux version (i686)
Automatically generated by the ATOMS compilation chain
Upload date : 2016年06月22日 17:38:31
MD5 : 750a526e4048682a66b41a673c9fd1e8
SHA1 : 7a711d60edb72d2d0f27e4f00a1f99378b7fe8f1
Downloads : 814
 
[221.50 kB]
Windows 32-bit binary for Scilab 5.5.x
Windows version (i686)
Automatically generated by the ATOMS compilation chain
Upload date : 2016年06月22日 17:38:34
MD5 : 83a96bf1d5d10e1ce705fff981766ea2
SHA1 : 3ba6d7ff7203ff7a1a6b808a90d2bfad595ccd0f
Downloads : 524
 
[210.62 kB]
Linux 64-bit binary for Scilab 5.5.x
Linux version (x86_64)
Automatically generated by the ATOMS compilation chain
Upload date : 2016年06月22日 17:38:29
MD5 : a00d63783e92f653c057221e4939c154
SHA1 : c8fff7cfaefe6917f55a7dbc481f7526a1910770
Downloads : 3122
 
News (0)
Comments (2) 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');

Comment from Syed Abu-Bakar -- February 20, 2019, 07:58:10 AM
Hi
Just wonder when will this toolbox be available for Scilab 6.0. I've already removed Scilab
5.5 from my PC as I don't want to keep more than 1 version in my laptop.

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