Support Vector Machine Implementation

In this article, we present a method called Support Vector Machine (SVM) implemented using MATLAB programming. Support Vector Machine represents a widely used approach for classification detection and pattern recognition tasks. This method enables accurate data classification and recognition, thereby providing valuable insights for data analysis. The SVM algorithm operates by finding the optimal hyperplane that maximizes the margin between different classes in the feature space. Through model training, SVM learns the distinguishing characteristics between various categories and assigns new data points to their correct classifications with high accuracy.

MATLAB serves as a powerful programming language and development environment that facilitates the implementation of SVM algorithms. Key functions like fitcsvm for binary classification or fitcecoc for multi-class classification provide efficient SVM implementations. The platform supports essential data preprocessing, kernel function selection (linear, polynomial, RBF), and parameter optimization through built-in toolbox functions. By integrating SVM methodology with MATLAB's computational capabilities, researchers can perform more precise and reliable classification detection and pattern recognition tasks. The implementation typically involves data normalization, kernel parameter tuning using cross-validation, and model evaluation through metrics like accuracy, precision, and recall.