The research of the fast SVM classifier method

Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization of empirical risk and confidence range in the case of small statistical sample size and it can also obtain the desired good statistical law.