Support Vector Machine as a Novel Regression Approach

Support Vector Machine (SVM) represents a novel regression methodology especially well-suited for nonlinear problems. It addresses nonlinearity by constructing a high-dimensional feature space through kernel functions and performs regression analysis using support vectors. This method has gained widespread practical application, particularly in data mining and pattern recognition domains. The program implements SVM nonlinear regression functionality utilizing key algorithms including kernel trick implementation (such as RBF or polynomial kernels) and optimization techniques like sequential minimal optimization (SMO). The implementation effectively handles nonlinear regression analysis by mapping input data to higher-dimensional spaces where linear regression becomes feasible. Key functions include data normalization, kernel matrix computation, and support vector selection, which collectively enable accurate prediction results. Thus, SVM nonlinear regression serves as a powerful and reliable tool, particularly valuable for addressing complex nonlinear regression challenges through its robust mathematical foundation and computational efficiency.