MATLAB Implementation of Support Vector Machine with Code Examples

This discussion elaborates on the implementation of Support Vector Machine (SVM) source code. SVM represents a widely-used machine learning algorithm for both classification and regression tasks, fundamentally based on identifying an optimal hyperplane that maximally separates data points from different classes. The implementation utilizes the libsvm open-source library, which requires proper MATLAB interface configuration through mex-file compilation. For enhanced classification performance, critical parameters require optimization through techniques like grid search with cross-validation. Key optimizable parameters include: kernel function selection (linear, polynomial, RBF, or sigmoid), penalty parameter C (controlling misclassification tolerance), and kernel-specific parameters like gamma for RBF kernels. The MATLAB implementation typically involves data normalization, model training using svmtrain function, and prediction through svmpredict. Parameter tuning can be automated using libsvm's grid.py script or MATLAB's fminsearch function, significantly improving model accuracy and generalization capabilities across diverse datasets.