An Improvement of One-Against-One Method for Multi-Class Support Vector Machine

The support vector machine (SVM) has an excellent ability to solve binary classification problems. How to process multi-class problems with SVM is one of the present focuses. Among the existing multi-class SVM methods include one-against-one method, one-against-all method and some others. This paper presents an improved technique of one-against-one method that can largely reduce the number of the hyper-planes and speed up the predicting process. The experimental results show that the proposed method not only has promising accuracy and less training time, but also significantly improves the predicting speed in comparison with traditional one-against-one and one-against-all method.