MATLAB Implementation of Support Vector Machine with Code Examples

This document presents a comprehensive implementation of Support Vector Machine (SVM) in MATLAB environment. SVM is a powerful supervised machine learning algorithm widely employed in image processing and facial recognition systems. The implementation utilizes MATLAB's built-in functions like fitcsvm for binary classification and fitcecoc for multi-class problems, with customizable kernel functions (linear, polynomial, RBF) through kernel parameters. Key implementation aspects include: - Data preprocessing using z-score normalization for feature scaling - Cross-validation techniques via cvpartition function for model evaluation - Hyperparameter optimization using Bayesian optimization or grid search - Feature extraction methods specific to image data (HOG, LBP) for facial recognition tasks The code demonstrates practical applications in image classification by converting images into feature vectors and employing SVM for pattern recognition. For face recognition tasks, the implementation includes Principal Component Analysis (PCA) for dimensionality reduction before SVM classification. Performance metrics such as accuracy, precision, and recall are calculated using confusionmat function to validate model effectiveness. Users can experiment with different kernel parameters and penalty factors (C-value) to optimize separation margins. The code includes visualization components using plot functions to display decision boundaries and support vectors. This implementation provides a solid foundation for developing robust image classification systems with demonstrated efficacy in facial recognition applications.