Download and extract libsvm in a directory of your choosing, say C:\libsvm

As described in the C:\libsvm\matlab\README file, first you have to make sure a supported C/C++ compiler is installed. Note that on 64-bit systems, you need the correct 64-bit version of the compiler (e.g. Windows SDK is needed for Visual Studio Express edition)

>> mex -setup

Once you have selected a compiler, you need to compile the MEX-files:

>> cd('C:\libsvm\matlab')
>> make

Finally add the folder with the generated binaries to the MATLAB search path:

>> addpath('C:\libsvm\matlab')

Test the library with a simple example (fake data):

>> labels = double(rand(10,1)>0.5);
>> data = rand(10,5);
>> model = svmtrain(labels, data, '-s 0 -t 2 -c 1 -g 0.1')

Note that the current version of libsvm includes pre-compiled 64-bit MEX-files for Windows. The binaries are located in C:\libsvm\windows\*.mexw64 (copy those to the matlab subfolder from above)

For the time being, it does not matter where you put that folder. You'll have to compile the Matlab-MEX files, from the source code that resides in the matlab subfolder of your extracted libsvm package.

Below, I will call the libsvm-3.14 (for example: C:\libsvm-3.14) extracted folder ROOT (so replace ROOT wilth C:\libsvm-3.14 below).

So, fire up Matlab and, then, at its command prompt, type:

cd ROOT
cd matlab
make

Now, in the ROOT/matlab folder, you should have obtained two MEX files (with the .mexw32 or .mexw64 extension). These are the files that you will use from now on. You can discard the rest, except those files (and, maybe, some other generated libraries).

According to the Makefile (that is ROOT/matlab/make.m), the MEX files are called svmtrain and svmpredic (+ the extension).

So, next step would be to create a subfolder called libsvm/ under your's Matlab/toolbox folder, copy the two MEX files therein and, then, add that libsvm/ folder to the Matlab Path (not the Path of your system, but of your Matlab installation).

Then, by typing:

svmtrain

and

svmpredict

you should have the help (usage) of these two functions.

In libsvm package, in the file matlab/README, you can find the following examples:

Examples
========

Train and test on the provided data heart_scale:

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07');
matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data

For probability estimates, you need '-b 1' for training and testing:

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1');
matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1');

To use precomputed kernel, you must include sample serial number as
the first column of the training and testing data (assume your kernel
matrix is K, # of instances is n):

matlab> K1 = [(1:n)', K]; % include sample serial number as first column
matlab> model = svmtrain(label_vector, K1, '-t 4');
matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data

We give the following detailed example by splitting heart_scale into
150 training and 120 testing data.  Constructing a linear kernel
matrix and then using the precomputed kernel gives exactly the same
testing error as using the LIBSVM built-in linear kernel.

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale');
matlab>
matlab> % Split Data
matlab> train_data = heart_scale_inst(1:150,:);
matlab> train_label = heart_scale_label(1:150,:);
matlab> test_data = heart_scale_inst(151:270,:);
matlab> test_label = heart_scale_label(151:270,:);
matlab>
matlab> % Linear Kernel
matlab> model_linear = svmtrain(train_label, train_data, '-t 0');
matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear);
matlab>
matlab> % Precomputed Kernel
matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4');
matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed);
matlab>
matlab> accuracy_L % Display the accuracy using linear kernel
matlab> accuracy_P % Display the accuracy using precomputed kernel

Note that for testing, you can put anything in the
testing_label_vector.  For more details of precomputed kernels, please
read the section ``Precomputed Kernels'' in the README of the LIBSVM
package.