Multi-Class SVM Classifier MATLAB Implementation

This MATLAB implementation provides a multi-class Support Vector Machine (SVM) classifier code. The classifier employs SVM algorithms to handle classification tasks across multiple categories. Support Vector Machine represents a powerful machine learning algorithm particularly effective for solving classification problems. In this MATLAB code, we implement a multi-class classifier using SVM methodology to achieve accurate classification across different categories. The implementation typically involves using MATLAB's built-in functions like fitcecoc for multi-class classification, which employs error-correcting output codes (ECOC) framework combined with binary SVM classifiers. The code may include features for data preprocessing, parameter optimization using functions like fitcsvm, and performance evaluation through confusion matrices and cross-validation techniques. Key implementation aspects include kernel function selection (linear, polynomial, or RBF), cost parameter tuning, and handling class imbalance through appropriate weighting strategies.