Abstract
In recent years, support vector machine learning methods have gradually become the main research direction of machine learning. The support vector machine has a small structural risk compared with the traditional learning method, which can make the training error and the classifier capacity reach a relatively balanced state. Secondly, it also has the advantages of strong adaptability and strong promotion ability and has been widely praised by the industry. The following discussion focuses on the application of support vector machine in machine learning.