Title :
An Improvement of One-Against-One Method for Multi-Class Support Vector Machine
Author :
Liu, Yang ; Wang, Rui ; Zeng, Ying-sheng
Author_Institution :
Nat. Univ. of Defense Technol., Changsha
Abstract :
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.
Keywords :
pattern classification; support vector machines; binary classification problem; multiclass problem; one-against-one method; support vector machine; Automation; Computer science; Cybernetics; Electronic mail; Machine learning; Mechatronics; Pattern classification; Speech recognition; Support vector machine classification; Support vector machines; Multi-class problems; One-against-one method; Support vector machine (SVM);
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370646
