Least Squares Support Vector Machine

This code snippet provides a straightforward implementation of Least Squares Support Vector Machine (LS-SVM), suitable for various machine learning applications. The implementation utilizes a simplified approach to solving the optimization problem by converting inequality constraints into equality constraints through least squares methodology. While the algorithm may require significant computation time due to matrix inversion operations involved in solving the linear system, its performance efficiency often justifies the computational cost. We recommend incorporating detailed inline comments explaining key components such as the kernel function selection (e.g., linear or RBF kernels), regularization parameter tuning, and the matrix solution process using techniques like Cholesky decomposition. Furthermore, users can enhance understanding by adding comparative analysis with alternative algorithms such as standard SVM or kernel ridge regression, which would better demonstrate LS-SVM's practical advantages in handling regression and classification tasks with reduced complexity.