LIBSVM is abbreviated from library for support vector machines. It supports many programming languages including MATLAB, python, and etc. In this note I write down the steps needed for MATLAB R2012a on Mac OS X 10.9.

I downloaded the livsvm zip, but I don't understand the instructions included. Poncho sanchez conga blue rar. I have placed the libsvm.dll in my c: windows system32 directory, but when I try. Nov 20, 2016 - error at installing libsvm on windows7. Learn more about libsvm, make.

Before compilation of LIBSVM, you have to install Xcode 5.x and the command line tools. After the installation, type gcc --version in the terminal and you should see the output below. Next we must modify mexopts.sh in the MATLAB’s bin directory. This file specifies the compilers and include paths.

In the first section of the file, there is a variable called ARCH. This variable represents the architecture of the computer, which is maci64 in this case. Take a look at the part of maci64 around line 160. Four variables are wrong: CC, CXX, SDKROOT, and MACOSX_DEPLOYMENT_TARGET. They should be modified as the following: CC='gcc' SDKROOT='/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.9.sdk' MACOSX_DEPLOYMENT_TARGET='10.9' CXX='g++' You may need to change the permission of the file so that you can modify it. Now we can compile LIBSVM.

$ cd libsvm-3.17/matlab $ matlab -nodisplay >> make GUI is not necessary, so just turn it off to make the process faster. The output is as the following: After the compilation, four files are added to the folder: libsvmread.mexmaci64, libsvmwrite.mexmaci64, svmpredict.mexmaci64, and svmtrain.mexmaci64. For testing, use the code in README. Here is the result. If you want to make LIBSVM loaded at the start, create a folder libsvm-3.17 at toolbox.

Then add a line at toolbox/local/startup.m: addpath('/Applications/MATLAB_R2012a.app/toolbox/libsvm-3.17/').

This tool provides a simple interface to LIBSVM, a library for support vector machines (~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 (~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! 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').