A New SVM Multi-Class Classification Method Based on Error-Correcting Code

Traditional SVM (support vector machine) multi-class classification methods are mainly based on one-to-one and one-to-multi, which both have disadvantages in applications: slow computational speed and low classification precision. This paper introduces a new method based on error correcting code to reduce the training time and improve the classification precision. In view of the relations among the length, the Hsmming distance and the order of the code and the generalization ability of each SVM, we propose the principles of code table-designing and the center-range method that ascertains the code order to eliminate the problem caused by error correcting code in factual application. Finally the results of experiments of HRRP recognition show this improved method has high computational efficiency and batter generalization ability.