Optimized Least Squares Support Vector Machine Implementation

This MATLAB program code implements an optimized version of least squares support vector machines (LS-SVM). The implementation includes core SVM functionalities while offering additional advanced features. Key implementation aspects include: - Flexible kernel function selection (linear, polynomial, RBF) through configurable parameters to adapt to different dataset characteristics - Built-in cross-validation module for automated hyperparameter optimization using grid search or Bayesian optimization methods - Visualization tools for data distribution analysis and model performance evaluation, including decision boundary plots and convergence curves - Efficient matrix computation implementation leveraging MATLAB's optimized linear algebra libraries for handling large-scale datasets The code architecture employs modular design with separate functions for data preprocessing, model training, prediction, and validation. If you require a flexible, user-friendly, and powerful SVM implementation with robust optimization capabilities, this code provides a comprehensive solution suitable for both research and practical applications.