SVM Multi-Classification Algorithm Implementation in MATLAB

This is a MATLAB-based implementation of a Support Vector Machine (SVM) multi-classification algorithm program. Support Vector Machines represent a widely-used machine learning algorithm for solving classification problems. By employing SVM algorithms, we can effectively classify and predict data across multiple categories. This program is designed to facilitate the implementation and application of multi-classification algorithms within the MATLAB environment. The implementation likely utilizes MATLAB's built-in SVM functions or custom-coded solutions for handling multi-class problems through approaches like One-vs-One (OVO) or One-vs-All (OVA) strategies. Key programming aspects may include data preprocessing, kernel function selection (linear, polynomial, RBF), parameter optimization using grid search or cross-validation, and the use of MATLAB's classification tools for model training and prediction. The code structure probably incorporates feature scaling, model validation techniques, and performance evaluation metrics to ensure robust multi-class classification capabilities.