MATLAB Implementation of Support Vector Machine Method

In this documentation, we can utilize the Support Vector Machine (SVM) method to perform tasks such as classification detection, pattern recognition, and face detection. SVM is a widely-used machine learning algorithm that plays a crucial role across various domains. Using MATLAB's powerful computational environment, we can efficiently implement SVM through functions like fitcsvm for classification problems or fitrsvm for regression tasks. The implementation typically involves key steps: data preprocessing using zscore or normalize functions, kernel selection (linear, polynomial, or RBF) via the 'KernelFunction' parameter, and hyperparameter optimization using cross-validation with crossval. Through SVM methodology, we can achieve more accurate classification and recognition results, significantly improving model performance and prediction accuracy. The algorithm works by finding the optimal hyperplane that maximizes the margin between different classes using quadratic programming optimization. Therefore, the Support Vector Machine method serves as a highly valuable technique with substantial applications across numerous fields, particularly when implemented with MATLAB's comprehensive machine learning toolbox.