Least Squares Support Vector Machine (LSSVM) - Core Implementation

This repository provides the core implementation of Least Squares Support Vector Machines (LSSVM), representing the fundamental algorithmic framework. The program is designed to solve both classification and regression problems through MATLAB code implementation. For classification tasks, it utilizes a quadratic programming approach to separate datasets into two or more distinct classes by finding optimal hyperplanes. In regression applications, the algorithm employs a least squares methodology to predict continuous numerical outputs by minimizing squared error loss. The base implementation includes essential components such as: + Kernel function handlers (linear, RBF, polynomial) for non-linear mapping + Parameter optimization routines for regularization and kernel parameters + Matrix computation methods for solving linear systems efficiently Beyond this foundational version, extended implementations offer enhancements for handling complex scenarios through: + Advanced kernel selection algorithms for optimal feature space transformation + Automated hyperparameter tuning using cross-validation techniques + Feature selection modules incorporating filtering and wrapper methods + Multi-class classification extensions using one-vs-all strategies For developers seeking to deepen their understanding or address more sophisticated problems, we recommend exploring these advanced modules that demonstrate practical implementations of LSSVM optimization techniques and real-world application adaptations.