SVM (Support Vector Machine) - Binary and Multiclass Classification with MATLAB Implementation

In the provided context, we can further expand and explain key concepts. Support Vector Machine (SVM) is a machine learning algorithm designed for both binary classification and multiclass problems. For binary classification, SVM works by finding an optimal hyperplane that separates data into two distinct classes with maximum margin. For multiclass classification problems, two primary approaches are commonly implemented: One-vs-One (OvO) and One-vs-Rest (OvR). The One-vs-One method trains a binary classifier for every pair of classes, and final classification is determined through a voting mechanism among all pairwise comparisons. The One-vs-Rest approach trains multiple classifiers where each classifier distinguishes one class from all remaining classes combined. In MATLAB implementation, key functions like fitcsvm for binary classification and fitcecoc for multiclass problems are typically used. The code implementation generally involves these steps: - Data preprocessing and normalization using functions like zscore or normalize - Feature extraction and selection techniques - Model training with parameter optimization (kernel selection, cost parameter C, gamma tuning) - Cross-validation using cvpartition for model evaluation - Testing and performance metrics calculation (accuracy, precision, recall, F1-score) The MATLAB code provides a comprehensive framework for handling the entire machine learning pipeline, from data preparation to model deployment, with built-in functions for efficient SVM training and robust classification performance evaluation.