MATLAB Toolkit for Least Squares Support Vector Machines

This article introduces a highly practical MATLAB toolkit designed for Least Squares Support Vector Machines (LS-SVM). The toolkit contains fully implemented source code and comprehensive usage instructions to facilitate better understanding and application of this important machine learning algorithm. The source code includes robust functions for efficient LS-SVM implementation, featuring core components such as kernel function computations (linear, RBF, polynomial), parameter optimization routines, and training/prediction modules. The implementation follows the LS-SVM formulation that converts standard SVM inequality constraints into equality constraints through least squares error minimization. The documentation provides detailed explanations of each function's purpose, parameter tuning methodologies (including regularization and kernel parameter selection), and practical examples demonstrating data preprocessing, model training, and result validation techniques. Overall, this toolkit serves as an essential resource for effectively implementing LS-SVM algorithms, significantly enhancing workflow efficiency and result quality through its well-structured codebase and thorough technical guidance.