Support Vector Machine

Support vector machine can seek the best compromise between the complexity of the model and its learning ability based on limited sample information, thereby enabling the model to achieve the best generalization ability. Support vector machine exhibits many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems, and are widely applied to other machine learning issues such as function fitting. Support Vector Machine (SVM) was proposed by Cortes et al. in 1995. Because of its excellent performance in text classification tasks, SVM soon became the mainstream technology of machine learning, and directly set off the climax of “statistical learning” around 2000.