MATLAB Implementation of Support Vector Machine with Code Examples

This article provides a comprehensive MATLAB implementation of Support Vector Machine (SVM), including several practical usage examples. Support Vector Machine is a widely-used machine learning algorithm for both classification and regression problems. The core principle involves finding an optimal hyperplane that maximizes the margin between different classes of data points to achieve effective classification. The program implements several common SVM variants, including: - Linear SVM: Suitable for linearly separable data using a simple linear kernel function - Gaussian Kernel SVM: Handles non-linear classification problems through radial basis function (RBF) kernel transformation Key implementation features include: - Data preprocessing and normalization routines - Kernel function selection and parameter configuration - Model training with optimization algorithms - Cross-validation and performance evaluation metrics The code structure allows users to easily train and test SVM models for various real-world applications. The implementation includes clear function documentation and parameter explanations to facilitate customization. Users can modify kernel parameters, adjust regularization constants, and implement custom validation strategies. We hope this program serves as a valuable resource for your machine learning studies and research projects, providing both practical implementation insights and theoretical understanding of SVM algorithms.