Implementing Classification and Function Regression using Support Vector Machines (SVM)

This is a source code example demonstrating how to implement classification and function regression using Support Vector Machines (SVM). The implementation includes core SVM components such as kernel function selection (linear, polynomial, or RBF), optimization for finding the optimal hyperplane, and handling both classification boundaries and regression curves. You can modify and extend the code according to your specific needs. Support Vector Machines represent a widely-used machine learning algorithm that delivers strong performance in both classification and regression problems. The code structure allows for easy experimentation with different datasets and parameter tuning through adjustable parameters like regularization constant C and kernel parameters. For those unfamiliar with SVM, the code includes clear comments and references to key algorithmic concepts like maximum margin optimization and support vector identification. We recommend consulting relevant literature and tutorials to better understand and apply this algorithm. This source code aims to support your research and help you achieve better results in machine learning applications!