MATLAB Code Implementation of SVM Toolbox

I have found MATLAB's SVM toolbox particularly efficient for classification tasks. To implement this, first place your dataset in the appropriate folder within the MATLAB directory. Launch MATLAB and execute the following configuration sequence: 1. Run 'mex setup' to configure the C/C++ compiler environment 2. Enter 'y' to confirm compiler selection 3. Input '2' to choose the installed compiler 4. Finalize with 'y' to complete setup. This process enables the toolbox to compile native extensions for accelerated SVM operations. The toolbox utilizes LIBSVM's core algorithms for support vector classification, implementing sequential minimal optimization (SMO) for efficient training. After configuration, you can load training samples to build the SVM model using svmtrain function, which handles kernel selection (linear/RBF/sigmoid) and parameter optimization. Testing samples are then evaluated through svmpredict to generate: 1) Classification accuracy percentage 2) Detailed confusion matrix showing true vs predicted classifications. This implementation significantly enhances classification efficiency and accuracy through optimized C/MATLAB integration.