Support Vector Regression
Support Vector Machine (SVM), first proposed by Corinna Cortes and Vapnik in 1995, demonstrates unique advantages in solving small-sample, nonlinear, and high-dimensional pattern recognition problems. It can be extended to other machine learning tasks such as function fitting. In machine learning, SVM is a supervised learning model that analyzes data and recognizes patterns for classification and regression analysis. Key implementation aspects include kernel selection and margin optimization algorithms.