Support Vector Regression

In 1995, Corinna Cortes and Vapnik first introduced the Support Vector Machine (SVM) model. This approach demonstrates exceptional advantages in handling small-sample, nonlinear, and high-dimensional pattern recognition problems, and can be extended to other machine learning applications such as function fitting. In the field of machine learning, Support Vector Machine (also known as support vector networks) is a supervised learning model associated with relevant learning algorithms that analyzes data and recognizes patterns for both classification and regression tasks. SVM can handle complex datasets, including those in high-dimensional spaces, making it a valuable tool for solving many practical problems. The implementation typically involves solving a convex optimization problem to find the optimal hyperplane that maximizes the margin between classes. For regression tasks (SVR), the model tolerates small deviations using epsilon-insensitive loss functions. Furthermore, SVM's performance can be enhanced by selecting appropriate kernel functions (such as linear, polynomial, or radial basis function kernels) to adapt to different data types and problem characteristics. The kernel trick allows SVM to efficiently handle nonlinear relationships by mapping data to higher-dimensional spaces without explicit transformation. Overall, Support Vector Machine represents a powerful and flexible machine learning method that enables deeper data analysis and more accurate predictions. Key parameters in implementation include the regularization parameter C, kernel parameters, and epsilon value for regression tasks, which control the trade-off between model complexity and error tolerance.