Study of Svm-based Incremental Learning for User Adaptation in Multi-class Classification Environment

SVM-based incremental learning, which can make a user-relevant recognition system quickly adapt to specific users' preference without losing its general performance, is an elegant solution for user adaptation problems in on-line graphics recognition system. Two learning strategies (repetitive learning and incremental learning), two incremental learning algorithms (Syed et al.'s and Xiao et al.'s), and two classifier structures (one-against-one and one-against-all) are compared under the multi-class classification environment. Theoretical analysis and experimental results both show: (1) incremental learning can adapt the classifier to new obtained samples much faster than repetitive learning without losing any precision; (2) the SVM-based incremental learning algorithm of Syed et al.'s is superior to that of Xiao et al's; (3) one-against-one structure is superior to one-against-all structure for a multi-class incremental learning environment.