MATLAB Implementation of Support Vector Machine with Iris Dataset Classification

This article presents a Support Vector Machine program developed in MATLAB, which was experimentally validated on the iris dataset with outstanding recognition results. The implementation utilizes MATLAB's machine learning capabilities to perform classification by analyzing dataset features and generating predictions based on input data. The code likely employs kernel functions (such as linear or RBF kernels) for feature transformation and optimization algorithms for finding the optimal hyperplane that maximizes the margin between different classes. The program's accuracy and efficiency make it a valuable tool applicable across various domains including medical diagnosis, financial forecasting, and engineering applications. The modular design ensures high extensibility, allowing users to modify and optimize parameters like kernel selection, regularization constants, or tolerance thresholds to meet specific task requirements. The implementation probably includes data preprocessing steps, cross-validation routines, and performance evaluation metrics to ensure robustness. Overall, this SVM program serves as a powerful and reliable tool that significantly contributes to data analysis and pattern recognition tasks, providing a solid foundation for both academic research and practical applications.