The Development and Application of Support Vector Machine

Abstract

Support Vector Machine(SVM) algorithm has the advantages of complete theory, global optimization, strong adaptability, and good generalization ability because of it on the basis of Statistical Learning Theory's(SLT). It is a new hot spot in machine learning research. This article first systematically studies some basic concepts of SVM and the optimization of SVM. In addition, this article also discusses the application of SVM in modern machining, protein prediction and face detection. Through these applications, the performance characteristics and advantages of SVM can be reflected. At the end of the article, some shortcomings of SVM are introduced, and the development trend of subsequent SVM is pointed out accordingly.