A novel SVM multi-class classifier based on pairwise coupling
Zeyu Li, Shiwei Tang, Jing Xue · 2003
In this paper, a novel algorithm is proposed to tackle multi-class classification problems. For a K-class classification task, an array of K optimal pairwise coupling classifiers (O-PWC) is constructed, each of which is optimal to the corresponding class and provides a reliable probability estimation for that class. The classification accuracy rate is improved while the computational cost does not increase too much. At the same time, a more accurate estimation of posterior probabilities for a given pattern can be acquired. This algorithm is applied to face recognition on an ORL face database. Experimental results reveal that our method is effective and efficient.